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/* Loop Vectorization
Copyright (C) 2003-2018 Free Software Foundation, Inc.
Contributed by Dorit Naishlos <dorit@il.ibm.com> and
Ira Rosen <irar@il.ibm.com>
This file is part of GCC.
GCC is free software; you can redistribute it and/or modify it under
the terms of the GNU General Public License as published by the Free
Software Foundation; either version 3, or (at your option) any later
version.
GCC is distributed in the hope that it will be useful, but WITHOUT ANY
WARRANTY; without even the implied warranty of MERCHANTABILITY or
FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License
for more details.
You should have received a copy of the GNU General Public License
along with GCC; see the file COPYING3. If not see
<http://www.gnu.org/licenses/>. */
#include "config.h"
#include "system.h"
#include "coretypes.h"
#include "backend.h"
#include "target.h"
#include "rtl.h"
#include "tree.h"
#include "gimple.h"
#include "cfghooks.h"
#include "tree-pass.h"
#include "ssa.h"
#include "optabs-tree.h"
#include "diagnostic-core.h"
#include "fold-const.h"
#include "stor-layout.h"
#include "cfganal.h"
#include "gimplify.h"
#include "gimple-iterator.h"
#include "gimplify-me.h"
#include "tree-ssa-loop-ivopts.h"
#include "tree-ssa-loop-manip.h"
#include "tree-ssa-loop-niter.h"
#include "tree-ssa-loop.h"
#include "cfgloop.h"
#include "params.h"
#include "tree-scalar-evolution.h"
#include "tree-vectorizer.h"
#include "gimple-fold.h"
#include "cgraph.h"
#include "tree-cfg.h"
#include "tree-if-conv.h"
#include "internal-fn.h"
#include "tree-vector-builder.h"
#include "vec-perm-indices.h"
#include "tree-eh.h"
/* Loop Vectorization Pass.
This pass tries to vectorize loops.
For example, the vectorizer transforms the following simple loop:
short a[N]; short b[N]; short c[N]; int i;
for (i=0; i<N; i++){
a[i] = b[i] + c[i];
}
as if it was manually vectorized by rewriting the source code into:
typedef int __attribute__((mode(V8HI))) v8hi;
short a[N]; short b[N]; short c[N]; int i;
v8hi *pa = (v8hi*)a, *pb = (v8hi*)b, *pc = (v8hi*)c;
v8hi va, vb, vc;
for (i=0; i<N/8; i++){
vb = pb[i];
vc = pc[i];
va = vb + vc;
pa[i] = va;
}
The main entry to this pass is vectorize_loops(), in which
the vectorizer applies a set of analyses on a given set of loops,
followed by the actual vectorization transformation for the loops that
had successfully passed the analysis phase.
Throughout this pass we make a distinction between two types of
data: scalars (which are represented by SSA_NAMES), and memory references
("data-refs"). These two types of data require different handling both
during analysis and transformation. The types of data-refs that the
vectorizer currently supports are ARRAY_REFS which base is an array DECL
(not a pointer), and INDIRECT_REFS through pointers; both array and pointer
accesses are required to have a simple (consecutive) access pattern.
Analysis phase:
===============
The driver for the analysis phase is vect_analyze_loop().
It applies a set of analyses, some of which rely on the scalar evolution
analyzer (scev) developed by Sebastian Pop.
During the analysis phase the vectorizer records some information
per stmt in a "stmt_vec_info" struct which is attached to each stmt in the
loop, as well as general information about the loop as a whole, which is
recorded in a "loop_vec_info" struct attached to each loop.
Transformation phase:
=====================
The loop transformation phase scans all the stmts in the loop, and
creates a vector stmt (or a sequence of stmts) for each scalar stmt S in
the loop that needs to be vectorized. It inserts the vector code sequence
just before the scalar stmt S, and records a pointer to the vector code
in STMT_VINFO_VEC_STMT (stmt_info) (stmt_info is the stmt_vec_info struct
attached to S). This pointer will be used for the vectorization of following
stmts which use the def of stmt S. Stmt S is removed if it writes to memory;
otherwise, we rely on dead code elimination for removing it.
For example, say stmt S1 was vectorized into stmt VS1:
VS1: vb = px[i];
S1: b = x[i]; STMT_VINFO_VEC_STMT (stmt_info (S1)) = VS1
S2: a = b;
To vectorize stmt S2, the vectorizer first finds the stmt that defines
the operand 'b' (S1), and gets the relevant vector def 'vb' from the
vector stmt VS1 pointed to by STMT_VINFO_VEC_STMT (stmt_info (S1)). The
resulting sequence would be:
VS1: vb = px[i];
S1: b = x[i]; STMT_VINFO_VEC_STMT (stmt_info (S1)) = VS1
VS2: va = vb;
S2: a = b; STMT_VINFO_VEC_STMT (stmt_info (S2)) = VS2
Operands that are not SSA_NAMEs, are data-refs that appear in
load/store operations (like 'x[i]' in S1), and are handled differently.
Target modeling:
=================
Currently the only target specific information that is used is the
size of the vector (in bytes) - "TARGET_VECTORIZE_UNITS_PER_SIMD_WORD".
Targets that can support different sizes of vectors, for now will need
to specify one value for "TARGET_VECTORIZE_UNITS_PER_SIMD_WORD". More
flexibility will be added in the future.
Since we only vectorize operations which vector form can be
expressed using existing tree codes, to verify that an operation is
supported, the vectorizer checks the relevant optab at the relevant
machine_mode (e.g, optab_handler (add_optab, V8HImode)). If
the value found is CODE_FOR_nothing, then there's no target support, and
we can't vectorize the stmt.
For additional information on this project see:
http://gcc.gnu.org/projects/tree-ssa/vectorization.html
*/
static void vect_estimate_min_profitable_iters (loop_vec_info, int *, int *);
/* Function vect_determine_vectorization_factor
Determine the vectorization factor (VF). VF is the number of data elements
that are operated upon in parallel in a single iteration of the vectorized
loop. For example, when vectorizing a loop that operates on 4byte elements,
on a target with vector size (VS) 16byte, the VF is set to 4, since 4
elements can fit in a single vector register.
We currently support vectorization of loops in which all types operated upon
are of the same size. Therefore this function currently sets VF according to
the size of the types operated upon, and fails if there are multiple sizes
in the loop.
VF is also the factor by which the loop iterations are strip-mined, e.g.:
original loop:
for (i=0; i<N; i++){
a[i] = b[i] + c[i];
}
vectorized loop:
for (i=0; i<N; i+=VF){
a[i:VF] = b[i:VF] + c[i:VF];
}
*/
static bool
vect_determine_vectorization_factor (loop_vec_info loop_vinfo)
{
struct loop *loop = LOOP_VINFO_LOOP (loop_vinfo);
basic_block *bbs = LOOP_VINFO_BBS (loop_vinfo);
unsigned nbbs = loop->num_nodes;
poly_uint64 vectorization_factor = 1;
tree scalar_type = NULL_TREE;
gphi *phi;
tree vectype;
stmt_vec_info stmt_info;
unsigned i;
HOST_WIDE_INT dummy;
gimple *stmt, *pattern_stmt = NULL;
gimple_seq pattern_def_seq = NULL;
gimple_stmt_iterator pattern_def_si = gsi_none ();
bool analyze_pattern_stmt = false;
bool bool_result;
auto_vec<stmt_vec_info> mask_producers;
if (dump_enabled_p ())
dump_printf_loc (MSG_NOTE, vect_location,
"=== vect_determine_vectorization_factor ===\n");
for (i = 0; i < nbbs; i++)
{
basic_block bb = bbs[i];
for (gphi_iterator si = gsi_start_phis (bb); !gsi_end_p (si);
gsi_next (&si))
{
phi = si.phi ();
stmt_info = vinfo_for_stmt (phi);
if (dump_enabled_p ())
{
dump_printf_loc (MSG_NOTE, vect_location, "==> examining phi: ");
dump_gimple_stmt (MSG_NOTE, TDF_SLIM, phi, 0);
}
gcc_assert (stmt_info);
if (STMT_VINFO_RELEVANT_P (stmt_info)
|| STMT_VINFO_LIVE_P (stmt_info))
{
gcc_assert (!STMT_VINFO_VECTYPE (stmt_info));
scalar_type = TREE_TYPE (PHI_RESULT (phi));
if (dump_enabled_p ())
{
dump_printf_loc (MSG_NOTE, vect_location,
"get vectype for scalar type: ");
dump_generic_expr (MSG_NOTE, TDF_SLIM, scalar_type);
dump_printf (MSG_NOTE, "\n");
}
vectype = get_vectype_for_scalar_type (scalar_type);
if (!vectype)
{
if (dump_enabled_p ())
{
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"not vectorized: unsupported "
"data-type ");
dump_generic_expr (MSG_MISSED_OPTIMIZATION, TDF_SLIM,
scalar_type);
dump_printf (MSG_MISSED_OPTIMIZATION, "\n");
}
return false;
}
STMT_VINFO_VECTYPE (stmt_info) = vectype;
if (dump_enabled_p ())
{
dump_printf_loc (MSG_NOTE, vect_location, "vectype: ");
dump_generic_expr (MSG_NOTE, TDF_SLIM, vectype);
dump_printf (MSG_NOTE, "\n");
}
if (dump_enabled_p ())
{
dump_printf_loc (MSG_NOTE, vect_location, "nunits = ");
dump_dec (MSG_NOTE, TYPE_VECTOR_SUBPARTS (vectype));
dump_printf (MSG_NOTE, "\n");
}
vect_update_max_nunits (&vectorization_factor, vectype);
}
}
for (gimple_stmt_iterator si = gsi_start_bb (bb);
!gsi_end_p (si) || analyze_pattern_stmt;)
{
tree vf_vectype;
if (analyze_pattern_stmt)
stmt = pattern_stmt;
else
stmt = gsi_stmt (si);
stmt_info = vinfo_for_stmt (stmt);
if (dump_enabled_p ())
{
dump_printf_loc (MSG_NOTE, vect_location,
"==> examining statement: ");
dump_gimple_stmt (MSG_NOTE, TDF_SLIM, stmt, 0);
}
gcc_assert (stmt_info);
/* Skip stmts which do not need to be vectorized. */
if ((!STMT_VINFO_RELEVANT_P (stmt_info)
&& !STMT_VINFO_LIVE_P (stmt_info))
|| gimple_clobber_p (stmt))
{
if (STMT_VINFO_IN_PATTERN_P (stmt_info)
&& (pattern_stmt = STMT_VINFO_RELATED_STMT (stmt_info))
&& (STMT_VINFO_RELEVANT_P (vinfo_for_stmt (pattern_stmt))
|| STMT_VINFO_LIVE_P (vinfo_for_stmt (pattern_stmt))))
{
stmt = pattern_stmt;
stmt_info = vinfo_for_stmt (pattern_stmt);
if (dump_enabled_p ())
{
dump_printf_loc (MSG_NOTE, vect_location,
"==> examining pattern statement: ");
dump_gimple_stmt (MSG_NOTE, TDF_SLIM, stmt, 0);
}
}
else
{
if (dump_enabled_p ())
dump_printf_loc (MSG_NOTE, vect_location, "skip.\n");
gsi_next (&si);
continue;
}
}
else if (STMT_VINFO_IN_PATTERN_P (stmt_info)
&& (pattern_stmt = STMT_VINFO_RELATED_STMT (stmt_info))
&& (STMT_VINFO_RELEVANT_P (vinfo_for_stmt (pattern_stmt))
|| STMT_VINFO_LIVE_P (vinfo_for_stmt (pattern_stmt))))
analyze_pattern_stmt = true;
/* If a pattern statement has def stmts, analyze them too. */
if (is_pattern_stmt_p (stmt_info))
{
if (pattern_def_seq == NULL)
{
pattern_def_seq = STMT_VINFO_PATTERN_DEF_SEQ (stmt_info);
pattern_def_si = gsi_start (pattern_def_seq);
}
else if (!gsi_end_p (pattern_def_si))
gsi_next (&pattern_def_si);
if (pattern_def_seq != NULL)
{
gimple *pattern_def_stmt = NULL;
stmt_vec_info pattern_def_stmt_info = NULL;
while (!gsi_end_p (pattern_def_si))
{
pattern_def_stmt = gsi_stmt (pattern_def_si);
pattern_def_stmt_info
= vinfo_for_stmt (pattern_def_stmt);
if (STMT_VINFO_RELEVANT_P (pattern_def_stmt_info)
|| STMT_VINFO_LIVE_P (pattern_def_stmt_info))
break;
gsi_next (&pattern_def_si);
}
if (!gsi_end_p (pattern_def_si))
{
if (dump_enabled_p ())
{
dump_printf_loc (MSG_NOTE, vect_location,
"==> examining pattern def stmt: ");
dump_gimple_stmt (MSG_NOTE, TDF_SLIM,
pattern_def_stmt, 0);
}
stmt = pattern_def_stmt;
stmt_info = pattern_def_stmt_info;
}
else
{
pattern_def_si = gsi_none ();
analyze_pattern_stmt = false;
}
}
else
analyze_pattern_stmt = false;
}
if (gimple_get_lhs (stmt) == NULL_TREE
/* MASK_STORE has no lhs, but is ok. */
&& (!is_gimple_call (stmt)
|| !gimple_call_internal_p (stmt)
|| gimple_call_internal_fn (stmt) != IFN_MASK_STORE))
{
if (is_gimple_call (stmt))
{
/* Ignore calls with no lhs. These must be calls to
#pragma omp simd functions, and what vectorization factor
it really needs can't be determined until
vectorizable_simd_clone_call. */
if (!analyze_pattern_stmt && gsi_end_p (pattern_def_si))
{
pattern_def_seq = NULL;
gsi_next (&si);
}
continue;
}
if (dump_enabled_p ())
{
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"not vectorized: irregular stmt.");
dump_gimple_stmt (MSG_MISSED_OPTIMIZATION, TDF_SLIM, stmt,
0);
}
return false;
}
if (VECTOR_MODE_P (TYPE_MODE (gimple_expr_type (stmt))))
{
if (dump_enabled_p ())
{
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"not vectorized: vector stmt in loop:");
dump_gimple_stmt (MSG_MISSED_OPTIMIZATION, TDF_SLIM, stmt, 0);
}
return false;
}
bool_result = false;
if (STMT_VINFO_VECTYPE (stmt_info))
{
/* The only case when a vectype had been already set is for stmts
that contain a dataref, or for "pattern-stmts" (stmts
generated by the vectorizer to represent/replace a certain
idiom). */
gcc_assert (STMT_VINFO_DATA_REF (stmt_info)
|| is_pattern_stmt_p (stmt_info)
|| !gsi_end_p (pattern_def_si));
vectype = STMT_VINFO_VECTYPE (stmt_info);
}
else
{
gcc_assert (!STMT_VINFO_DATA_REF (stmt_info));
if (gimple_call_internal_p (stmt, IFN_MASK_STORE))
scalar_type = TREE_TYPE (gimple_call_arg (stmt, 3));
else
scalar_type = TREE_TYPE (gimple_get_lhs (stmt));
/* Bool ops don't participate in vectorization factor
computation. For comparison use compared types to
compute a factor. */
if (VECT_SCALAR_BOOLEAN_TYPE_P (scalar_type)
&& is_gimple_assign (stmt)
&& gimple_assign_rhs_code (stmt) != COND_EXPR)
{
if (STMT_VINFO_RELEVANT_P (stmt_info)
|| STMT_VINFO_LIVE_P (stmt_info))
mask_producers.safe_push (stmt_info);
bool_result = true;
if (TREE_CODE_CLASS (gimple_assign_rhs_code (stmt))
== tcc_comparison
&& !VECT_SCALAR_BOOLEAN_TYPE_P
(TREE_TYPE (gimple_assign_rhs1 (stmt))))
scalar_type = TREE_TYPE (gimple_assign_rhs1 (stmt));
else
{
if (!analyze_pattern_stmt && gsi_end_p (pattern_def_si))
{
pattern_def_seq = NULL;
gsi_next (&si);
}
continue;
}
}
if (dump_enabled_p ())
{
dump_printf_loc (MSG_NOTE, vect_location,
"get vectype for scalar type: ");
dump_generic_expr (MSG_NOTE, TDF_SLIM, scalar_type);
dump_printf (MSG_NOTE, "\n");
}
vectype = get_vectype_for_scalar_type (scalar_type);
if (!vectype)
{
if (dump_enabled_p ())
{
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"not vectorized: unsupported "
"data-type ");
dump_generic_expr (MSG_MISSED_OPTIMIZATION, TDF_SLIM,
scalar_type);
dump_printf (MSG_MISSED_OPTIMIZATION, "\n");
}
return false;
}
if (!bool_result)
STMT_VINFO_VECTYPE (stmt_info) = vectype;
if (dump_enabled_p ())
{
dump_printf_loc (MSG_NOTE, vect_location, "vectype: ");
dump_generic_expr (MSG_NOTE, TDF_SLIM, vectype);
dump_printf (MSG_NOTE, "\n");
}
}
/* Don't try to compute VF out scalar types if we stmt
produces boolean vector. Use result vectype instead. */
if (VECTOR_BOOLEAN_TYPE_P (vectype))
vf_vectype = vectype;
else
{
/* The vectorization factor is according to the smallest
scalar type (or the largest vector size, but we only
support one vector size per loop). */
if (!bool_result)
scalar_type = vect_get_smallest_scalar_type (stmt, &dummy,
&dummy);
if (dump_enabled_p ())
{
dump_printf_loc (MSG_NOTE, vect_location,
"get vectype for scalar type: ");
dump_generic_expr (MSG_NOTE, TDF_SLIM, scalar_type);
dump_printf (MSG_NOTE, "\n");
}
vf_vectype = get_vectype_for_scalar_type (scalar_type);
}
if (!vf_vectype)
{
if (dump_enabled_p ())
{
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"not vectorized: unsupported data-type ");
dump_generic_expr (MSG_MISSED_OPTIMIZATION, TDF_SLIM,
scalar_type);
dump_printf (MSG_MISSED_OPTIMIZATION, "\n");
}
return false;
}
if (maybe_ne (GET_MODE_SIZE (TYPE_MODE (vectype)),
GET_MODE_SIZE (TYPE_MODE (vf_vectype))))
{
if (dump_enabled_p ())
{
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"not vectorized: different sized vector "
"types in statement, ");
dump_generic_expr (MSG_MISSED_OPTIMIZATION, TDF_SLIM,
vectype);
dump_printf (MSG_MISSED_OPTIMIZATION, " and ");
dump_generic_expr (MSG_MISSED_OPTIMIZATION, TDF_SLIM,
vf_vectype);
dump_printf (MSG_MISSED_OPTIMIZATION, "\n");
}
return false;
}
if (dump_enabled_p ())
{
dump_printf_loc (MSG_NOTE, vect_location, "vectype: ");
dump_generic_expr (MSG_NOTE, TDF_SLIM, vf_vectype);
dump_printf (MSG_NOTE, "\n");
}
if (dump_enabled_p ())
{
dump_printf_loc (MSG_NOTE, vect_location, "nunits = ");
dump_dec (MSG_NOTE, TYPE_VECTOR_SUBPARTS (vf_vectype));
dump_printf (MSG_NOTE, "\n");
}
vect_update_max_nunits (&vectorization_factor, vf_vectype);
if (!analyze_pattern_stmt && gsi_end_p (pattern_def_si))
{
pattern_def_seq = NULL;
gsi_next (&si);
}
}
}
/* TODO: Analyze cost. Decide if worth while to vectorize. */
if (dump_enabled_p ())
{
dump_printf_loc (MSG_NOTE, vect_location, "vectorization factor = ");
dump_dec (MSG_NOTE, vectorization_factor);
dump_printf (MSG_NOTE, "\n");
}
if (known_le (vectorization_factor, 1U))
{
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"not vectorized: unsupported data-type\n");
return false;
}
LOOP_VINFO_VECT_FACTOR (loop_vinfo) = vectorization_factor;
for (i = 0; i < mask_producers.length (); i++)
{
tree mask_type = NULL;
stmt = STMT_VINFO_STMT (mask_producers[i]);
if (is_gimple_assign (stmt)
&& TREE_CODE_CLASS (gimple_assign_rhs_code (stmt)) == tcc_comparison
&& !VECT_SCALAR_BOOLEAN_TYPE_P
(TREE_TYPE (gimple_assign_rhs1 (stmt))))
{
scalar_type = TREE_TYPE (gimple_assign_rhs1 (stmt));
mask_type = get_mask_type_for_scalar_type (scalar_type);
if (!mask_type)
{
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"not vectorized: unsupported mask\n");
return false;
}
}
else
{
tree rhs;
ssa_op_iter iter;
gimple *def_stmt;
enum vect_def_type dt;
FOR_EACH_SSA_TREE_OPERAND (rhs, stmt, iter, SSA_OP_USE)
{
if (!vect_is_simple_use (rhs, mask_producers[i]->vinfo,
&def_stmt, &dt, &vectype))
{
if (dump_enabled_p ())
{
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"not vectorized: can't compute mask type "
"for statement, ");
dump_gimple_stmt (MSG_MISSED_OPTIMIZATION, TDF_SLIM, stmt,
0);
}
return false;
}
/* No vectype probably means external definition.
Allow it in case there is another operand which
allows to determine mask type. */
if (!vectype)
continue;
if (!mask_type)
mask_type = vectype;
else if (maybe_ne (TYPE_VECTOR_SUBPARTS (mask_type),
TYPE_VECTOR_SUBPARTS (vectype)))
{
if (dump_enabled_p ())
{
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"not vectorized: different sized masks "
"types in statement, ");
dump_generic_expr (MSG_MISSED_OPTIMIZATION, TDF_SLIM,
mask_type);
dump_printf (MSG_MISSED_OPTIMIZATION, " and ");
dump_generic_expr (MSG_MISSED_OPTIMIZATION, TDF_SLIM,
vectype);
dump_printf (MSG_MISSED_OPTIMIZATION, "\n");
}
return false;
}
else if (VECTOR_BOOLEAN_TYPE_P (mask_type)
!= VECTOR_BOOLEAN_TYPE_P (vectype))
{
if (dump_enabled_p ())
{
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"not vectorized: mixed mask and "
"nonmask vector types in statement, ");
dump_generic_expr (MSG_MISSED_OPTIMIZATION, TDF_SLIM,
mask_type);
dump_printf (MSG_MISSED_OPTIMIZATION, " and ");
dump_generic_expr (MSG_MISSED_OPTIMIZATION, TDF_SLIM,
vectype);
dump_printf (MSG_MISSED_OPTIMIZATION, "\n");
}
return false;
}
}
/* We may compare boolean value loaded as vector of integers.
Fix mask_type in such case. */
if (mask_type
&& !VECTOR_BOOLEAN_TYPE_P (mask_type)
&& gimple_code (stmt) == GIMPLE_ASSIGN
&& TREE_CODE_CLASS (gimple_assign_rhs_code (stmt)) == tcc_comparison)
mask_type = build_same_sized_truth_vector_type (mask_type);
}
/* No mask_type should mean loop invariant predicate.
This is probably a subject for optimization in
if-conversion. */
if (!mask_type)
{
if (dump_enabled_p ())
{
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"not vectorized: can't compute mask type "
"for statement, ");
dump_gimple_stmt (MSG_MISSED_OPTIMIZATION, TDF_SLIM, stmt,
0);
}
return false;
}
STMT_VINFO_VECTYPE (mask_producers[i]) = mask_type;
}
return true;
}
/* Function vect_is_simple_iv_evolution.
FORNOW: A simple evolution of an induction variables in the loop is
considered a polynomial evolution. */
static bool
vect_is_simple_iv_evolution (unsigned loop_nb, tree access_fn, tree * init,
tree * step)
{
tree init_expr;
tree step_expr;
tree evolution_part = evolution_part_in_loop_num (access_fn, loop_nb);
basic_block bb;
/* When there is no evolution in this loop, the evolution function
is not "simple". */
if (evolution_part == NULL_TREE)
return false;
/* When the evolution is a polynomial of degree >= 2
the evolution function is not "simple". */
if (tree_is_chrec (evolution_part))
return false;
step_expr = evolution_part;
init_expr = unshare_expr (initial_condition_in_loop_num (access_fn, loop_nb));
if (dump_enabled_p ())
{
dump_printf_loc (MSG_NOTE, vect_location, "step: ");
dump_generic_expr (MSG_NOTE, TDF_SLIM, step_expr);
dump_printf (MSG_NOTE, ", init: ");
dump_generic_expr (MSG_NOTE, TDF_SLIM, init_expr);
dump_printf (MSG_NOTE, "\n");
}
*init = init_expr;
*step = step_expr;
if (TREE_CODE (step_expr) != INTEGER_CST
&& (TREE_CODE (step_expr) != SSA_NAME
|| ((bb = gimple_bb (SSA_NAME_DEF_STMT (step_expr)))
&& flow_bb_inside_loop_p (get_loop (cfun, loop_nb), bb))
|| (!INTEGRAL_TYPE_P (TREE_TYPE (step_expr))
&& (!SCALAR_FLOAT_TYPE_P (TREE_TYPE (step_expr))
|| !flag_associative_math)))
&& (TREE_CODE (step_expr) != REAL_CST
|| !flag_associative_math))
{
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"step unknown.\n");
return false;
}
return true;
}
/* Function vect_analyze_scalar_cycles_1.
Examine the cross iteration def-use cycles of scalar variables
in LOOP. LOOP_VINFO represents the loop that is now being
considered for vectorization (can be LOOP, or an outer-loop
enclosing LOOP). */
static void
vect_analyze_scalar_cycles_1 (loop_vec_info loop_vinfo, struct loop *loop)
{
basic_block bb = loop->header;
tree init, step;
auto_vec<gimple *, 64> worklist;
gphi_iterator gsi;
bool double_reduc;
if (dump_enabled_p ())
dump_printf_loc (MSG_NOTE, vect_location,
"=== vect_analyze_scalar_cycles ===\n");
/* First - identify all inductions. Reduction detection assumes that all the
inductions have been identified, therefore, this order must not be
changed. */
for (gsi = gsi_start_phis (bb); !gsi_end_p (gsi); gsi_next (&gsi))
{
gphi *phi = gsi.phi ();
tree access_fn = NULL;
tree def = PHI_RESULT (phi);
stmt_vec_info stmt_vinfo = vinfo_for_stmt (phi);
if (dump_enabled_p ())
{
dump_printf_loc (MSG_NOTE, vect_location, "Analyze phi: ");
dump_gimple_stmt (MSG_NOTE, TDF_SLIM, phi, 0);
}
/* Skip virtual phi's. The data dependences that are associated with
virtual defs/uses (i.e., memory accesses) are analyzed elsewhere. */
if (virtual_operand_p (def))
continue;
STMT_VINFO_DEF_TYPE (stmt_vinfo) = vect_unknown_def_type;
/* Analyze the evolution function. */
access_fn = analyze_scalar_evolution (loop, def);
if (access_fn)
{
STRIP_NOPS (access_fn);
if (dump_enabled_p ())
{
dump_printf_loc (MSG_NOTE, vect_location,
"Access function of PHI: ");
dump_generic_expr (MSG_NOTE, TDF_SLIM, access_fn);
dump_printf (MSG_NOTE, "\n");
}
STMT_VINFO_LOOP_PHI_EVOLUTION_BASE_UNCHANGED (stmt_vinfo)
= initial_condition_in_loop_num (access_fn, loop->num);
STMT_VINFO_LOOP_PHI_EVOLUTION_PART (stmt_vinfo)
= evolution_part_in_loop_num (access_fn, loop->num);
}
if (!access_fn
|| !vect_is_simple_iv_evolution (loop->num, access_fn, &init, &step)
|| (LOOP_VINFO_LOOP (loop_vinfo) != loop
&& TREE_CODE (step) != INTEGER_CST))
{
worklist.safe_push (phi);
continue;
}
gcc_assert (STMT_VINFO_LOOP_PHI_EVOLUTION_BASE_UNCHANGED (stmt_vinfo)
!= NULL_TREE);
gcc_assert (STMT_VINFO_LOOP_PHI_EVOLUTION_PART (stmt_vinfo) != NULL_TREE);
if (dump_enabled_p ())
dump_printf_loc (MSG_NOTE, vect_location, "Detected induction.\n");
STMT_VINFO_DEF_TYPE (stmt_vinfo) = vect_induction_def;
}
/* Second - identify all reductions and nested cycles. */
while (worklist.length () > 0)
{
gimple *phi = worklist.pop ();
tree def = PHI_RESULT (phi);
stmt_vec_info stmt_vinfo = vinfo_for_stmt (phi);
gimple *reduc_stmt;
if (dump_enabled_p ())
{
dump_printf_loc (MSG_NOTE, vect_location, "Analyze phi: ");
dump_gimple_stmt (MSG_NOTE, TDF_SLIM, phi, 0);
}
gcc_assert (!virtual_operand_p (def)
&& STMT_VINFO_DEF_TYPE (stmt_vinfo) == vect_unknown_def_type);
reduc_stmt = vect_force_simple_reduction (loop_vinfo, phi,
&double_reduc, false);
if (reduc_stmt)
{
if (double_reduc)
{
if (dump_enabled_p ())
dump_printf_loc (MSG_NOTE, vect_location,
"Detected double reduction.\n");
STMT_VINFO_DEF_TYPE (stmt_vinfo) = vect_double_reduction_def;
STMT_VINFO_DEF_TYPE (vinfo_for_stmt (reduc_stmt)) =
vect_double_reduction_def;
}
else
{
if (loop != LOOP_VINFO_LOOP (loop_vinfo))
{
if (dump_enabled_p ())
dump_printf_loc (MSG_NOTE, vect_location,
"Detected vectorizable nested cycle.\n");
STMT_VINFO_DEF_TYPE (stmt_vinfo) = vect_nested_cycle;
STMT_VINFO_DEF_TYPE (vinfo_for_stmt (reduc_stmt)) =
vect_nested_cycle;
}
else
{
if (dump_enabled_p ())
dump_printf_loc (MSG_NOTE, vect_location,
"Detected reduction.\n");
STMT_VINFO_DEF_TYPE (stmt_vinfo) = vect_reduction_def;
STMT_VINFO_DEF_TYPE (vinfo_for_stmt (reduc_stmt)) =
vect_reduction_def;
/* Store the reduction cycles for possible vectorization in
loop-aware SLP if it was not detected as reduction
chain. */
if (! GROUP_FIRST_ELEMENT (vinfo_for_stmt (reduc_stmt)))
LOOP_VINFO_REDUCTIONS (loop_vinfo).safe_push (reduc_stmt);
}
}
}
else
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"Unknown def-use cycle pattern.\n");
}
}
/* Function vect_analyze_scalar_cycles.
Examine the cross iteration def-use cycles of scalar variables, by
analyzing the loop-header PHIs of scalar variables. Classify each
cycle as one of the following: invariant, induction, reduction, unknown.
We do that for the loop represented by LOOP_VINFO, and also to its
inner-loop, if exists.
Examples for scalar cycles:
Example1: reduction:
loop1:
for (i=0; i<N; i++)
sum += a[i];
Example2: induction:
loop2:
for (i=0; i<N; i++)
a[i] = i; */
static void
vect_analyze_scalar_cycles (loop_vec_info loop_vinfo)
{
struct loop *loop = LOOP_VINFO_LOOP (loop_vinfo);
vect_analyze_scalar_cycles_1 (loop_vinfo, loop);
/* When vectorizing an outer-loop, the inner-loop is executed sequentially.
Reductions in such inner-loop therefore have different properties than
the reductions in the nest that gets vectorized:
1. When vectorized, they are executed in the same order as in the original
scalar loop, so we can't change the order of computation when
vectorizing them.
2. FIXME: Inner-loop reductions can be used in the inner-loop, so the
current checks are too strict. */
if (loop->inner)
vect_analyze_scalar_cycles_1 (loop_vinfo, loop->inner);
}
/* Transfer group and reduction information from STMT to its pattern stmt. */
static void
vect_fixup_reduc_chain (gimple *stmt)
{
gimple *firstp = STMT_VINFO_RELATED_STMT (vinfo_for_stmt (stmt));
gimple *stmtp;
gcc_assert (!GROUP_FIRST_ELEMENT (vinfo_for_stmt (firstp))
&& GROUP_FIRST_ELEMENT (vinfo_for_stmt (stmt)));
GROUP_SIZE (vinfo_for_stmt (firstp)) = GROUP_SIZE (vinfo_for_stmt (stmt));
do
{
stmtp = STMT_VINFO_RELATED_STMT (vinfo_for_stmt (stmt));
GROUP_FIRST_ELEMENT (vinfo_for_stmt (stmtp)) = firstp;
stmt = GROUP_NEXT_ELEMENT (vinfo_for_stmt (stmt));
if (stmt)
GROUP_NEXT_ELEMENT (vinfo_for_stmt (stmtp))
= STMT_VINFO_RELATED_STMT (vinfo_for_stmt (stmt));
}
while (stmt);
STMT_VINFO_DEF_TYPE (vinfo_for_stmt (stmtp)) = vect_reduction_def;
}
/* Fixup scalar cycles that now have their stmts detected as patterns. */
static void
vect_fixup_scalar_cycles_with_patterns (loop_vec_info loop_vinfo)
{
gimple *first;
unsigned i;
FOR_EACH_VEC_ELT (LOOP_VINFO_REDUCTION_CHAINS (loop_vinfo), i, first)
if (STMT_VINFO_IN_PATTERN_P (vinfo_for_stmt (first)))
{
gimple *next = GROUP_NEXT_ELEMENT (vinfo_for_stmt (first));
while (next)
{
if (! STMT_VINFO_IN_PATTERN_P (vinfo_for_stmt (next)))
break;
next = GROUP_NEXT_ELEMENT (vinfo_for_stmt (next));
}
/* If not all stmt in the chain are patterns try to handle
the chain without patterns. */
if (! next)
{
vect_fixup_reduc_chain (first);
LOOP_VINFO_REDUCTION_CHAINS (loop_vinfo)[i]
= STMT_VINFO_RELATED_STMT (vinfo_for_stmt (first));
}
}
}
/* Function vect_get_loop_niters.
Determine how many iterations the loop is executed and place it
in NUMBER_OF_ITERATIONS. Place the number of latch iterations
in NUMBER_OF_ITERATIONSM1. Place the condition under which the
niter information holds in ASSUMPTIONS.
Return the loop exit condition. */
static gcond *
vect_get_loop_niters (struct loop *loop, tree *assumptions,
tree *number_of_iterations, tree *number_of_iterationsm1)
{
edge exit = single_exit (loop);
struct tree_niter_desc niter_desc;
tree niter_assumptions, niter, may_be_zero;
gcond *cond = get_loop_exit_condition (loop);
*assumptions = boolean_true_node;
*number_of_iterationsm1 = chrec_dont_know;
*number_of_iterations = chrec_dont_know;
if (dump_enabled_p ())
dump_printf_loc (MSG_NOTE, vect_location,
"=== get_loop_niters ===\n");
if (!exit)
return cond;
niter = chrec_dont_know;
may_be_zero = NULL_TREE;
niter_assumptions = boolean_true_node;
if (!number_of_iterations_exit_assumptions (loop, exit, &niter_desc, NULL)
|| chrec_contains_undetermined (niter_desc.niter))
return cond;
niter_assumptions = niter_desc.assumptions;
may_be_zero = niter_desc.may_be_zero;
niter = niter_desc.niter;
if (may_be_zero && integer_zerop (may_be_zero))
may_be_zero = NULL_TREE;
if (may_be_zero)
{
if (COMPARISON_CLASS_P (may_be_zero))
{
/* Try to combine may_be_zero with assumptions, this can simplify
computation of niter expression. */
if (niter_assumptions && !integer_nonzerop (niter_assumptions))
niter_assumptions = fold_build2 (TRUTH_AND_EXPR, boolean_type_node,
niter_assumptions,
fold_build1 (TRUTH_NOT_EXPR,
boolean_type_node,
may_be_zero));
else
niter = fold_build3 (COND_EXPR, TREE_TYPE (niter), may_be_zero,
build_int_cst (TREE_TYPE (niter), 0),
rewrite_to_non_trapping_overflow (niter));
may_be_zero = NULL_TREE;
}
else if (integer_nonzerop (may_be_zero))
{
*number_of_iterationsm1 = build_int_cst (TREE_TYPE (niter), 0);
*number_of_iterations = build_int_cst (TREE_TYPE (niter), 1);
return cond;
}
else
return cond;
}
*assumptions = niter_assumptions;
*number_of_iterationsm1 = niter;
/* We want the number of loop header executions which is the number
of latch executions plus one.
??? For UINT_MAX latch executions this number overflows to zero
for loops like do { n++; } while (n != 0); */
if (niter && !chrec_contains_undetermined (niter))
niter = fold_build2 (PLUS_EXPR, TREE_TYPE (niter), unshare_expr (niter),
build_int_cst (TREE_TYPE (niter), 1));
*number_of_iterations = niter;
return cond;
}
/* Function bb_in_loop_p
Used as predicate for dfs order traversal of the loop bbs. */
static bool
bb_in_loop_p (const_basic_block bb, const void *data)
{
const struct loop *const loop = (const struct loop *)data;
if (flow_bb_inside_loop_p (loop, bb))
return true;
return false;
}
/* Create and initialize a new loop_vec_info struct for LOOP_IN, as well as
stmt_vec_info structs for all the stmts in LOOP_IN. */
_loop_vec_info::_loop_vec_info (struct loop *loop_in)
: vec_info (vec_info::loop, init_cost (loop_in)),
loop (loop_in),
bbs (XCNEWVEC (basic_block, loop->num_nodes)),
num_itersm1 (NULL_TREE),
num_iters (NULL_TREE),
num_iters_unchanged (NULL_TREE),
num_iters_assumptions (NULL_TREE),
th (0),
versioning_threshold (0),
vectorization_factor (0),
max_vectorization_factor (0),
mask_skip_niters (NULL_TREE),
mask_compare_type (NULL_TREE),
unaligned_dr (NULL),
peeling_for_alignment (0),
ptr_mask (0),
ivexpr_map (NULL),
slp_unrolling_factor (1),
single_scalar_iteration_cost (0),
vectorizable (false),
can_fully_mask_p (true),
fully_masked_p (false),
peeling_for_gaps (false),
peeling_for_niter (false),
operands_swapped (false),
no_data_dependencies (false),
has_mask_store (false),
scalar_loop (NULL),
orig_loop_info (NULL)
{
/* Create/Update stmt_info for all stmts in the loop. */
basic_block *body = get_loop_body (loop);
for (unsigned int i = 0; i < loop->num_nodes; i++)
{
basic_block bb = body[i];
gimple_stmt_iterator si;
for (si = gsi_start_phis (bb); !gsi_end_p (si); gsi_next (&si))
{
gimple *phi = gsi_stmt (si);
gimple_set_uid (phi, 0);
set_vinfo_for_stmt (phi, new_stmt_vec_info (phi, this));
}
for (si = gsi_start_bb (bb); !gsi_end_p (si); gsi_next (&si))
{
gimple *stmt = gsi_stmt (si);
gimple_set_uid (stmt, 0);
set_vinfo_for_stmt (stmt, new_stmt_vec_info (stmt, this));
}
}
free (body);
/* CHECKME: We want to visit all BBs before their successors (except for
latch blocks, for which this assertion wouldn't hold). In the simple
case of the loop forms we allow, a dfs order of the BBs would the same
as reversed postorder traversal, so we are safe. */
unsigned int nbbs = dfs_enumerate_from (loop->header, 0, bb_in_loop_p,
bbs, loop->num_nodes, loop);
gcc_assert (nbbs == loop->num_nodes);
}
/* Free all levels of MASKS. */
void
release_vec_loop_masks (vec_loop_masks *masks)
{
rgroup_masks *rgm;
unsigned int i;
FOR_EACH_VEC_ELT (*masks, i, rgm)
rgm->masks.release ();
masks->release ();
}
/* Free all memory used by the _loop_vec_info, as well as all the
stmt_vec_info structs of all the stmts in the loop. */
_loop_vec_info::~_loop_vec_info ()
{
int nbbs;
gimple_stmt_iterator si;
int j;
nbbs = loop->num_nodes;
for (j = 0; j < nbbs; j++)
{
basic_block bb = bbs[j];
for (si = gsi_start_phis (bb); !gsi_end_p (si); gsi_next (&si))
free_stmt_vec_info (gsi_stmt (si));
for (si = gsi_start_bb (bb); !gsi_end_p (si); )
{
gimple *stmt = gsi_stmt (si);
/* We may have broken canonical form by moving a constant
into RHS1 of a commutative op. Fix such occurrences. */
if (operands_swapped && is_gimple_assign (stmt))
{
enum tree_code code = gimple_assign_rhs_code (stmt);
if ((code == PLUS_EXPR
|| code == POINTER_PLUS_EXPR
|| code == MULT_EXPR)
&& CONSTANT_CLASS_P (gimple_assign_rhs1 (stmt)))
swap_ssa_operands (stmt,
gimple_assign_rhs1_ptr (stmt),
gimple_assign_rhs2_ptr (stmt));
else if (code == COND_EXPR
&& CONSTANT_CLASS_P (gimple_assign_rhs2 (stmt)))
{
tree cond_expr = gimple_assign_rhs1 (stmt);
enum tree_code cond_code = TREE_CODE (cond_expr);
if (TREE_CODE_CLASS (cond_code) == tcc_comparison)
{
bool honor_nans = HONOR_NANS (TREE_OPERAND (cond_expr,
0));
cond_code = invert_tree_comparison (cond_code,
honor_nans);
if (cond_code != ERROR_MARK)
{
TREE_SET_CODE (cond_expr, cond_code);
swap_ssa_operands (stmt,
gimple_assign_rhs2_ptr (stmt),
gimple_assign_rhs3_ptr (stmt));
}
}
}
}
/* Free stmt_vec_info. */
free_stmt_vec_info (stmt);
gsi_next (&si);
}
}
free (bbs);
release_vec_loop_masks (&masks);
delete ivexpr_map;
loop->aux = NULL;
}
/* Return an invariant or register for EXPR and emit necessary
computations in the LOOP_VINFO loop preheader. */
tree
cse_and_gimplify_to_preheader (loop_vec_info loop_vinfo, tree expr)
{
if (is_gimple_reg (expr)
|| is_gimple_min_invariant (expr))
return expr;
if (! loop_vinfo->ivexpr_map)
loop_vinfo->ivexpr_map = new hash_map<tree_operand_hash, tree>;
tree &cached = loop_vinfo->ivexpr_map->get_or_insert (expr);
if (! cached)
{
gimple_seq stmts = NULL;
cached = force_gimple_operand (unshare_expr (expr),
&stmts, true, NULL_TREE);
if (stmts)
{
edge e = loop_preheader_edge (LOOP_VINFO_LOOP (loop_vinfo));
gsi_insert_seq_on_edge_immediate (e, stmts);
}
}
return cached;
}
/* Return true if we can use CMP_TYPE as the comparison type to produce
all masks required to mask LOOP_VINFO. */
static bool
can_produce_all_loop_masks_p (loop_vec_info loop_vinfo, tree cmp_type)
{
rgroup_masks *rgm;
unsigned int i;
FOR_EACH_VEC_ELT (LOOP_VINFO_MASKS (loop_vinfo), i, rgm)
if (rgm->mask_type != NULL_TREE
&& !direct_internal_fn_supported_p (IFN_WHILE_ULT,
cmp_type, rgm->mask_type,
OPTIMIZE_FOR_SPEED))
return false;
return true;
}
/* Calculate the maximum number of scalars per iteration for every
rgroup in LOOP_VINFO. */
static unsigned int
vect_get_max_nscalars_per_iter (loop_vec_info loop_vinfo)
{
unsigned int res = 1;
unsigned int i;
rgroup_masks *rgm;
FOR_EACH_VEC_ELT (LOOP_VINFO_MASKS (loop_vinfo), i, rgm)
res = MAX (res, rgm->max_nscalars_per_iter);
return res;
}
/* Each statement in LOOP_VINFO can be masked where necessary. Check
whether we can actually generate the masks required. Return true if so,
storing the type of the scalar IV in LOOP_VINFO_MASK_COMPARE_TYPE. */
static bool
vect_verify_full_masking (loop_vec_info loop_vinfo)
{
struct loop *loop = LOOP_VINFO_LOOP (loop_vinfo);
unsigned int min_ni_width;
/* Use a normal loop if there are no statements that need masking.
This only happens in rare degenerate cases: it means that the loop
has no loads, no stores, and no live-out values. */
if (LOOP_VINFO_MASKS (loop_vinfo).is_empty ())
return false;
/* Get the maximum number of iterations that is representable
in the counter type. */
tree ni_type = TREE_TYPE (LOOP_VINFO_NITERSM1 (loop_vinfo));
widest_int max_ni = wi::to_widest (TYPE_MAX_VALUE (ni_type)) + 1;
/* Get a more refined estimate for the number of iterations. */
widest_int max_back_edges;
if (max_loop_iterations (loop, &max_back_edges))
max_ni = wi::smin (max_ni, max_back_edges + 1);
/* Account for rgroup masks, in which each bit is replicated N times. */
max_ni *= vect_get_max_nscalars_per_iter (loop_vinfo);
/* Work out how many bits we need to represent the limit. */
min_ni_width = wi::min_precision (max_ni, UNSIGNED);
/* Find a scalar mode for which WHILE_ULT is supported. */
opt_scalar_int_mode cmp_mode_iter;
tree cmp_type = NULL_TREE;
FOR_EACH_MODE_IN_CLASS (cmp_mode_iter, MODE_INT)
{
unsigned int cmp_bits = GET_MODE_BITSIZE (cmp_mode_iter.require ());
if (cmp_bits >= min_ni_width
&& targetm.scalar_mode_supported_p (cmp_mode_iter.require ()))
{
tree this_type = build_nonstandard_integer_type (cmp_bits, true);
if (this_type
&& can_produce_all_loop_masks_p (loop_vinfo, this_type))
{
/* Although we could stop as soon as we find a valid mode,
it's often better to continue until we hit Pmode, since the
operands to the WHILE are more likely to be reusable in
address calculations. */
cmp_type = this_type;
if (cmp_bits >= GET_MODE_BITSIZE (Pmode))
break;
}
}
}
if (!cmp_type)
return false;
LOOP_VINFO_MASK_COMPARE_TYPE (loop_vinfo) = cmp_type;
return true;
}
/* Calculate the cost of one scalar iteration of the loop. */
static void
vect_compute_single_scalar_iteration_cost (loop_vec_info loop_vinfo)
{
struct loop *loop = LOOP_VINFO_LOOP (loop_vinfo);
basic_block *bbs = LOOP_VINFO_BBS (loop_vinfo);
int nbbs = loop->num_nodes, factor;
int innerloop_iters, i;
/* Gather costs for statements in the scalar loop. */
/* FORNOW. */
innerloop_iters = 1;
if (loop->inner)
innerloop_iters = 50; /* FIXME */
for (i = 0; i < nbbs; i++)
{
gimple_stmt_iterator si;
basic_block bb = bbs[i];
if (bb->loop_father == loop->inner)
factor = innerloop_iters;
else
factor = 1;
for (si = gsi_start_bb (bb); !gsi_end_p (si); gsi_next (&si))
{
gimple *stmt = gsi_stmt (si);
stmt_vec_info stmt_info = vinfo_for_stmt (stmt);
if (!is_gimple_assign (stmt) && !is_gimple_call (stmt))
continue;
/* Skip stmts that are not vectorized inside the loop. */
if (stmt_info
&& !STMT_VINFO_RELEVANT_P (stmt_info)
&& (!STMT_VINFO_LIVE_P (stmt_info)
|| !VECTORIZABLE_CYCLE_DEF (STMT_VINFO_DEF_TYPE (stmt_info)))
&& !STMT_VINFO_IN_PATTERN_P (stmt_info))
continue;
vect_cost_for_stmt kind;
if (STMT_VINFO_DATA_REF (stmt_info))
{
if (DR_IS_READ (STMT_VINFO_DATA_REF (stmt_info)))
kind = scalar_load;
else
kind = scalar_store;
}
else
kind = scalar_stmt;
record_stmt_cost (&LOOP_VINFO_SCALAR_ITERATION_COST (loop_vinfo),
factor, kind, stmt_info, 0, vect_prologue);
}
}
/* Now accumulate cost. */
void *target_cost_data = init_cost (loop);
stmt_info_for_cost *si;
int j;
FOR_EACH_VEC_ELT (LOOP_VINFO_SCALAR_ITERATION_COST (loop_vinfo),
j, si)
{
struct _stmt_vec_info *stmt_info
= si->stmt ? vinfo_for_stmt (si->stmt) : NULL;
(void) add_stmt_cost (target_cost_data, si->count,
si->kind, stmt_info, si->misalign,
vect_body);
}
unsigned dummy, body_cost = 0;
finish_cost (target_cost_data, &dummy, &body_cost, &dummy);
destroy_cost_data (target_cost_data);
LOOP_VINFO_SINGLE_SCALAR_ITERATION_COST (loop_vinfo) = body_cost;
}
/* Function vect_analyze_loop_form_1.
Verify that certain CFG restrictions hold, including:
- the loop has a pre-header
- the loop has a single entry and exit
- the loop exit condition is simple enough
- the number of iterations can be analyzed, i.e, a countable loop. The
niter could be analyzed under some assumptions. */
bool
vect_analyze_loop_form_1 (struct loop *loop, gcond **loop_cond,
tree *assumptions, tree *number_of_iterationsm1,
tree *number_of_iterations, gcond **inner_loop_cond)
{
if (dump_enabled_p ())
dump_printf_loc (MSG_NOTE, vect_location,
"=== vect_analyze_loop_form ===\n");
/* Different restrictions apply when we are considering an inner-most loop,
vs. an outer (nested) loop.
(FORNOW. May want to relax some of these restrictions in the future). */
if (!loop->inner)
{
/* Inner-most loop. We currently require that the number of BBs is
exactly 2 (the header and latch). Vectorizable inner-most loops
look like this:
(pre-header)
|
header <--------+
| | |
| +--> latch --+
|
(exit-bb) */
if (loop->num_nodes != 2)
{
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"not vectorized: control flow in loop.\n");
return false;
}
if (empty_block_p (loop->header))
{
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"not vectorized: empty loop.\n");
return false;
}
}
else
{
struct loop *innerloop = loop->inner;
edge entryedge;
/* Nested loop. We currently require that the loop is doubly-nested,
contains a single inner loop, and the number of BBs is exactly 5.
Vectorizable outer-loops look like this:
(pre-header)
|
header <---+
| |
inner-loop |
| |
tail ------+
|
(exit-bb)
The inner-loop has the properties expected of inner-most loops
as described above. */
if ((loop->inner)->inner || (loop->inner)->next)
{
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"not vectorized: multiple nested loops.\n");
return false;
}
if (loop->num_nodes != 5)
{
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"not vectorized: control flow in loop.\n");
return false;
}
entryedge = loop_preheader_edge (innerloop);
if (entryedge->src != loop->header
|| !single_exit (innerloop)
|| single_exit (innerloop)->dest != EDGE_PRED (loop->latch, 0)->src)
{
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"not vectorized: unsupported outerloop form.\n");
return false;
}
/* Analyze the inner-loop. */
tree inner_niterm1, inner_niter, inner_assumptions;
if (! vect_analyze_loop_form_1 (loop->inner, inner_loop_cond,
&inner_assumptions, &inner_niterm1,
&inner_niter, NULL)
/* Don't support analyzing niter under assumptions for inner
loop. */
|| !integer_onep (inner_assumptions))
{
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"not vectorized: Bad inner loop.\n");
return false;
}
if (!expr_invariant_in_loop_p (loop, inner_niter))
{
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"not vectorized: inner-loop count not"
" invariant.\n");
return false;
}
if (dump_enabled_p ())
dump_printf_loc (MSG_NOTE, vect_location,
"Considering outer-loop vectorization.\n");
}
if (!single_exit (loop)
|| EDGE_COUNT (loop->header->preds) != 2)
{
if (dump_enabled_p ())
{
if (!single_exit (loop))
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"not vectorized: multiple exits.\n");
else if (EDGE_COUNT (loop->header->preds) != 2)
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"not vectorized: too many incoming edges.\n");
}
return false;
}
/* We assume that the loop exit condition is at the end of the loop. i.e,
that the loop is represented as a do-while (with a proper if-guard
before the loop if needed), where the loop header contains all the
executable statements, and the latch is empty. */
if (!empty_block_p (loop->latch)
|| !gimple_seq_empty_p (phi_nodes (loop->latch)))
{
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"not vectorized: latch block not empty.\n");
return false;
}
/* Make sure the exit is not abnormal. */
edge e = single_exit (loop);
if (e->flags & EDGE_ABNORMAL)
{
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"not vectorized: abnormal loop exit edge.\n");
return false;
}
*loop_cond = vect_get_loop_niters (loop, assumptions, number_of_iterations,
number_of_iterationsm1);
if (!*loop_cond)
{
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"not vectorized: complicated exit condition.\n");
return false;
}
if (integer_zerop (*assumptions)
|| !*number_of_iterations
|| chrec_contains_undetermined (*number_of_iterations))
{
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"not vectorized: number of iterations cannot be "
"computed.\n");
return false;
}
if (integer_zerop (*number_of_iterations))
{
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"not vectorized: number of iterations = 0.\n");
return false;
}
return true;
}
/* Analyze LOOP form and return a loop_vec_info if it is of suitable form. */
loop_vec_info
vect_analyze_loop_form (struct loop *loop)
{
tree assumptions, number_of_iterations, number_of_iterationsm1;
gcond *loop_cond, *inner_loop_cond = NULL;
if (! vect_analyze_loop_form_1 (loop, &loop_cond,
&assumptions, &number_of_iterationsm1,
&number_of_iterations, &inner_loop_cond))
return NULL;
loop_vec_info loop_vinfo = new _loop_vec_info (loop);
LOOP_VINFO_NITERSM1 (loop_vinfo) = number_of_iterationsm1;
LOOP_VINFO_NITERS (loop_vinfo) = number_of_iterations;
LOOP_VINFO_NITERS_UNCHANGED (loop_vinfo) = number_of_iterations;
if (!integer_onep (assumptions))
{
/* We consider to vectorize this loop by versioning it under
some assumptions. In order to do this, we need to clear
existing information computed by scev and niter analyzer. */
scev_reset_htab ();
free_numbers_of_iterations_estimates (loop);
/* Also set flag for this loop so that following scev and niter
analysis are done under the assumptions. */
loop_constraint_set (loop, LOOP_C_FINITE);
/* Also record the assumptions for versioning. */
LOOP_VINFO_NITERS_ASSUMPTIONS (loop_vinfo) = assumptions;
}
if (!LOOP_VINFO_NITERS_KNOWN_P (loop_vinfo))
{
if (dump_enabled_p ())
{
dump_printf_loc (MSG_NOTE, vect_location,
"Symbolic number of iterations is ");
dump_generic_expr (MSG_NOTE, TDF_DETAILS, number_of_iterations);
dump_printf (MSG_NOTE, "\n");
}
}
STMT_VINFO_TYPE (vinfo_for_stmt (loop_cond)) = loop_exit_ctrl_vec_info_type;
if (inner_loop_cond)
STMT_VINFO_TYPE (vinfo_for_stmt (inner_loop_cond))
= loop_exit_ctrl_vec_info_type;
gcc_assert (!loop->aux);
loop->aux = loop_vinfo;
return loop_vinfo;
}
/* Scan the loop stmts and dependent on whether there are any (non-)SLP
statements update the vectorization factor. */
static void
vect_update_vf_for_slp (loop_vec_info loop_vinfo)
{
struct loop *loop = LOOP_VINFO_LOOP (loop_vinfo);
basic_block *bbs = LOOP_VINFO_BBS (loop_vinfo);
int nbbs = loop->num_nodes;
poly_uint64 vectorization_factor;
int i;
if (dump_enabled_p ())
dump_printf_loc (MSG_NOTE, vect_location,
"=== vect_update_vf_for_slp ===\n");
vectorization_factor = LOOP_VINFO_VECT_FACTOR (loop_vinfo);
gcc_assert (known_ne (vectorization_factor, 0U));
/* If all the stmts in the loop can be SLPed, we perform only SLP, and
vectorization factor of the loop is the unrolling factor required by
the SLP instances. If that unrolling factor is 1, we say, that we
perform pure SLP on loop - cross iteration parallelism is not
exploited. */
bool only_slp_in_loop = true;
for (i = 0; i < nbbs; i++)
{
basic_block bb = bbs[i];
for (gimple_stmt_iterator si = gsi_start_bb (bb); !gsi_end_p (si);
gsi_next (&si))
{
gimple *stmt = gsi_stmt (si);
stmt_vec_info stmt_info = vinfo_for_stmt (stmt);
if (STMT_VINFO_IN_PATTERN_P (stmt_info)
&& STMT_VINFO_RELATED_STMT (stmt_info))
{
stmt = STMT_VINFO_RELATED_STMT (stmt_info);
stmt_info = vinfo_for_stmt (stmt);
}
if ((STMT_VINFO_RELEVANT_P (stmt_info)
|| VECTORIZABLE_CYCLE_DEF (STMT_VINFO_DEF_TYPE (stmt_info)))
&& !PURE_SLP_STMT (stmt_info))
/* STMT needs both SLP and loop-based vectorization. */
only_slp_in_loop = false;
}
}
if (only_slp_in_loop)
{
dump_printf_loc (MSG_NOTE, vect_location,
"Loop contains only SLP stmts\n");
vectorization_factor = LOOP_VINFO_SLP_UNROLLING_FACTOR (loop_vinfo);
}
else
{
dump_printf_loc (MSG_NOTE, vect_location,
"Loop contains SLP and non-SLP stmts\n");
/* Both the vectorization factor and unroll factor have the form
current_vector_size * X for some rational X, so they must have
a common multiple. */
vectorization_factor
= force_common_multiple (vectorization_factor,
LOOP_VINFO_SLP_UNROLLING_FACTOR (loop_vinfo));
}
LOOP_VINFO_VECT_FACTOR (loop_vinfo) = vectorization_factor;
if (dump_enabled_p ())
{
dump_printf_loc (MSG_NOTE, vect_location,
"Updating vectorization factor to ");
dump_dec (MSG_NOTE, vectorization_factor);
dump_printf (MSG_NOTE, ".\n");
}
}
/* Return true if STMT_INFO describes a double reduction phi and if
the other phi in the reduction is also relevant for vectorization.
This rejects cases such as:
outer1:
x_1 = PHI <x_3(outer2), ...>;
...
inner:
x_2 = ...;
...
outer2:
x_3 = PHI <x_2(inner)>;
if nothing in x_2 or elsewhere makes x_1 relevant. */
static bool
vect_active_double_reduction_p (stmt_vec_info stmt_info)
{
if (STMT_VINFO_DEF_TYPE (stmt_info) != vect_double_reduction_def)
return false;
gimple *other_phi = STMT_VINFO_REDUC_DEF (stmt_info);
return STMT_VINFO_RELEVANT_P (vinfo_for_stmt (other_phi));
}
/* Function vect_analyze_loop_operations.
Scan the loop stmts and make sure they are all vectorizable. */
static bool
vect_analyze_loop_operations (loop_vec_info loop_vinfo)
{
struct loop *loop = LOOP_VINFO_LOOP (loop_vinfo);
basic_block *bbs = LOOP_VINFO_BBS (loop_vinfo);
int nbbs = loop->num_nodes;
int i;
stmt_vec_info stmt_info;
bool need_to_vectorize = false;
bool ok;
if (dump_enabled_p ())
dump_printf_loc (MSG_NOTE, vect_location,
"=== vect_analyze_loop_operations ===\n");
for (i = 0; i < nbbs; i++)
{
basic_block bb = bbs[i];
for (gphi_iterator si = gsi_start_phis (bb); !gsi_end_p (si);
gsi_next (&si))
{
gphi *phi = si.phi ();
ok = true;
stmt_info = vinfo_for_stmt (phi);
if (dump_enabled_p ())
{
dump_printf_loc (MSG_NOTE, vect_location, "examining phi: ");
dump_gimple_stmt (MSG_NOTE, TDF_SLIM, phi, 0);
}
if (virtual_operand_p (gimple_phi_result (phi)))
continue;
/* Inner-loop loop-closed exit phi in outer-loop vectorization
(i.e., a phi in the tail of the outer-loop). */
if (! is_loop_header_bb_p (bb))
{
/* FORNOW: we currently don't support the case that these phis
are not used in the outerloop (unless it is double reduction,
i.e., this phi is vect_reduction_def), cause this case
requires to actually do something here. */
if (STMT_VINFO_LIVE_P (stmt_info)
&& !vect_active_double_reduction_p (stmt_info))
{
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"Unsupported loop-closed phi in "
"outer-loop.\n");
return false;
}
/* If PHI is used in the outer loop, we check that its operand
is defined in the inner loop. */
if (STMT_VINFO_RELEVANT_P (stmt_info))
{
tree phi_op;
gimple *op_def_stmt;
if (gimple_phi_num_args (phi) != 1)
return false;
phi_op = PHI_ARG_DEF (phi, 0);
if (TREE_CODE (phi_op) != SSA_NAME)
return false;
op_def_stmt = SSA_NAME_DEF_STMT (phi_op);
if (gimple_nop_p (op_def_stmt)
|| !flow_bb_inside_loop_p (loop, gimple_bb (op_def_stmt))
|| !vinfo_for_stmt (op_def_stmt))
return false;
if (STMT_VINFO_RELEVANT (vinfo_for_stmt (op_def_stmt))
!= vect_used_in_outer
&& STMT_VINFO_RELEVANT (vinfo_for_stmt (op_def_stmt))
!= vect_used_in_outer_by_reduction)
return false;
}
continue;
}
gcc_assert (stmt_info);
if ((STMT_VINFO_RELEVANT (stmt_info) == vect_used_in_scope
|| STMT_VINFO_LIVE_P (stmt_info))
&& STMT_VINFO_DEF_TYPE (stmt_info) != vect_induction_def)
{
/* A scalar-dependence cycle that we don't support. */
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"not vectorized: scalar dependence cycle.\n");
return false;
}
if (STMT_VINFO_RELEVANT_P (stmt_info))
{
need_to_vectorize = true;
if (STMT_VINFO_DEF_TYPE (stmt_info) == vect_induction_def
&& ! PURE_SLP_STMT (stmt_info))
ok = vectorizable_induction (phi, NULL, NULL, NULL);
else if ((STMT_VINFO_DEF_TYPE (stmt_info) == vect_reduction_def
|| STMT_VINFO_DEF_TYPE (stmt_info) == vect_nested_cycle)
&& ! PURE_SLP_STMT (stmt_info))
ok = vectorizable_reduction (phi, NULL, NULL, NULL, NULL);
}
/* SLP PHIs are tested by vect_slp_analyze_node_operations. */
if (ok
&& STMT_VINFO_LIVE_P (stmt_info)
&& !PURE_SLP_STMT (stmt_info))
ok = vectorizable_live_operation (phi, NULL, NULL, -1, NULL);
if (!ok)
{
if (dump_enabled_p ())
{
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"not vectorized: relevant phi not "
"supported: ");
dump_gimple_stmt (MSG_MISSED_OPTIMIZATION, TDF_SLIM, phi, 0);
}
return false;
}
}
for (gimple_stmt_iterator si = gsi_start_bb (bb); !gsi_end_p (si);
gsi_next (&si))
{
gimple *stmt = gsi_stmt (si);
if (!gimple_clobber_p (stmt)
&& !vect_analyze_stmt (stmt, &need_to_vectorize, NULL, NULL))
return false;
}
} /* bbs */
/* All operations in the loop are either irrelevant (deal with loop
control, or dead), or only used outside the loop and can be moved
out of the loop (e.g. invariants, inductions). The loop can be
optimized away by scalar optimizations. We're better off not
touching this loop. */
if (!need_to_vectorize)
{
if (dump_enabled_p ())
dump_printf_loc (MSG_NOTE, vect_location,
"All the computation can be taken out of the loop.\n");
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"not vectorized: redundant loop. no profit to "
"vectorize.\n");
return false;
}
return true;
}
/* Analyze the cost of the loop described by LOOP_VINFO. Decide if it
is worthwhile to vectorize. Return 1 if definitely yes, 0 if
definitely no, or -1 if it's worth retrying. */
static int
vect_analyze_loop_costing (loop_vec_info loop_vinfo)
{
struct loop *loop = LOOP_VINFO_LOOP (loop_vinfo);
unsigned int assumed_vf = vect_vf_for_cost (loop_vinfo);
/* Only fully-masked loops can have iteration counts less than the
vectorization factor. */
if (!LOOP_VINFO_FULLY_MASKED_P (loop_vinfo))
{
HOST_WIDE_INT max_niter;
if (LOOP_VINFO_NITERS_KNOWN_P (loop_vinfo))
max_niter = LOOP_VINFO_INT_NITERS (loop_vinfo);
else
max_niter = max_stmt_executions_int (loop);
if (max_niter != -1
&& (unsigned HOST_WIDE_INT) max_niter < assumed_vf)
{
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"not vectorized: iteration count smaller than "
"vectorization factor.\n");
return 0;
}
}
int min_profitable_iters, min_profitable_estimate;
vect_estimate_min_profitable_iters (loop_vinfo, &min_profitable_iters,
&min_profitable_estimate);
if (min_profitable_iters < 0)
{
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"not vectorized: vectorization not profitable.\n");
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"not vectorized: vector version will never be "
"profitable.\n");
return -1;
}
int min_scalar_loop_bound = (PARAM_VALUE (PARAM_MIN_VECT_LOOP_BOUND)
* assumed_vf);
/* Use the cost model only if it is more conservative than user specified
threshold. */
unsigned int th = (unsigned) MAX (min_scalar_loop_bound,
min_profitable_iters);
LOOP_VINFO_COST_MODEL_THRESHOLD (loop_vinfo) = th;
if (LOOP_VINFO_NITERS_KNOWN_P (loop_vinfo)
&& LOOP_VINFO_INT_NITERS (loop_vinfo) < th)
{
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"not vectorized: vectorization not profitable.\n");
if (dump_enabled_p ())
dump_printf_loc (MSG_NOTE, vect_location,
"not vectorized: iteration count smaller than user "
"specified loop bound parameter or minimum profitable "
"iterations (whichever is more conservative).\n");
return 0;
}
HOST_WIDE_INT estimated_niter = estimated_stmt_executions_int (loop);
if (estimated_niter == -1)
estimated_niter = likely_max_stmt_executions_int (loop);
if (estimated_niter != -1
&& ((unsigned HOST_WIDE_INT) estimated_niter
< MAX (th, (unsigned) min_profitable_estimate)))
{
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"not vectorized: estimated iteration count too "
"small.\n");
if (dump_enabled_p ())
dump_printf_loc (MSG_NOTE, vect_location,
"not vectorized: estimated iteration count smaller "
"than specified loop bound parameter or minimum "
"profitable iterations (whichever is more "
"conservative).\n");
return -1;
}
return 1;
}
/* Function vect_analyze_loop_2.
Apply a set of analyses on LOOP, and create a loop_vec_info struct
for it. The different analyses will record information in the
loop_vec_info struct. */
static bool
vect_analyze_loop_2 (loop_vec_info loop_vinfo, bool &fatal)
{
bool ok;
int res;
unsigned int max_vf = MAX_VECTORIZATION_FACTOR;
poly_uint64 min_vf = 2;
unsigned int n_stmts = 0;
/* The first group of checks is independent of the vector size. */
fatal = true;
/* Find all data references in the loop (which correspond to vdefs/vuses)
and analyze their evolution in the loop. */
basic_block *bbs = LOOP_VINFO_BBS (loop_vinfo);
loop_p loop = LOOP_VINFO_LOOP (loop_vinfo);
if (!find_loop_nest (loop, &LOOP_VINFO_LOOP_NEST (loop_vinfo)))
{
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"not vectorized: loop nest containing two "
"or more consecutive inner loops cannot be "
"vectorized\n");
return false;
}
for (unsigned i = 0; i < loop->num_nodes; i++)
for (gimple_stmt_iterator gsi = gsi_start_bb (bbs[i]);
!gsi_end_p (gsi); gsi_next (&gsi))
{
gimple *stmt = gsi_stmt (gsi);
if (is_gimple_debug (stmt))
continue;
++n_stmts;
if (!find_data_references_in_stmt (loop, stmt,
&LOOP_VINFO_DATAREFS (loop_vinfo)))
{
if (is_gimple_call (stmt) && loop->safelen)
{
tree fndecl = gimple_call_fndecl (stmt), op;
if (fndecl != NULL_TREE)
{
cgraph_node *node = cgraph_node::get (fndecl);
if (node != NULL && node->simd_clones != NULL)
{
unsigned int j, n = gimple_call_num_args (stmt);
for (j = 0; j < n; j++)
{
op = gimple_call_arg (stmt, j);
if (DECL_P (op)
|| (REFERENCE_CLASS_P (op)
&& get_base_address (op)))
break;
}
op = gimple_call_lhs (stmt);
/* Ignore #pragma omp declare simd functions
if they don't have data references in the
call stmt itself. */
if (j == n
&& !(op
&& (DECL_P (op)
|| (REFERENCE_CLASS_P (op)
&& get_base_address (op)))))
continue;
}
}
}
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"not vectorized: loop contains function "
"calls or data references that cannot "
"be analyzed\n");
return false;
}
}
/* Analyze the data references and also adjust the minimal
vectorization factor according to the loads and stores. */
ok = vect_analyze_data_refs (loop_vinfo, &min_vf);
if (!ok)
{
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"bad data references.\n");
return false;
}
/* Classify all cross-iteration scalar data-flow cycles.
Cross-iteration cycles caused by virtual phis are analyzed separately. */
vect_analyze_scalar_cycles (loop_vinfo);
vect_pattern_recog (loop_vinfo);
vect_fixup_scalar_cycles_with_patterns (loop_vinfo);
/* Analyze the access patterns of the data-refs in the loop (consecutive,
complex, etc.). FORNOW: Only handle consecutive access pattern. */
ok = vect_analyze_data_ref_accesses (loop_vinfo);
if (!ok)
{
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"bad data access.\n");
return false;
}
/* Data-flow analysis to detect stmts that do not need to be vectorized. */
ok = vect_mark_stmts_to_be_vectorized (loop_vinfo);
if (!ok)
{
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"unexpected pattern.\n");
return false;
}
/* While the rest of the analysis below depends on it in some way. */
fatal = false;
/* Analyze data dependences between the data-refs in the loop
and adjust the maximum vectorization factor according to
the dependences.
FORNOW: fail at the first data dependence that we encounter. */
ok = vect_analyze_data_ref_dependences (loop_vinfo, &max_vf);
if (!ok
|| (max_vf != MAX_VECTORIZATION_FACTOR
&& maybe_lt (max_vf, min_vf)))
{
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"bad data dependence.\n");
return false;
}
LOOP_VINFO_MAX_VECT_FACTOR (loop_vinfo) = max_vf;
ok = vect_determine_vectorization_factor (loop_vinfo);
if (!ok)
{
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"can't determine vectorization factor.\n");
return false;
}
if (max_vf != MAX_VECTORIZATION_FACTOR
&& maybe_lt (max_vf, LOOP_VINFO_VECT_FACTOR (loop_vinfo)))
{
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"bad data dependence.\n");
return false;
}
/* Compute the scalar iteration cost. */
vect_compute_single_scalar_iteration_cost (loop_vinfo);
poly_uint64 saved_vectorization_factor = LOOP_VINFO_VECT_FACTOR (loop_vinfo);
unsigned th;
/* Check the SLP opportunities in the loop, analyze and build SLP trees. */
ok = vect_analyze_slp (loop_vinfo, n_stmts);
if (!ok)
return false;
/* If there are any SLP instances mark them as pure_slp. */
bool slp = vect_make_slp_decision (loop_vinfo);
if (slp)
{
/* Find stmts that need to be both vectorized and SLPed. */
vect_detect_hybrid_slp (loop_vinfo);
/* Update the vectorization factor based on the SLP decision. */
vect_update_vf_for_slp (loop_vinfo);
}
bool saved_can_fully_mask_p = LOOP_VINFO_CAN_FULLY_MASK_P (loop_vinfo);
/* We don't expect to have to roll back to anything other than an empty
set of rgroups. */
gcc_assert (LOOP_VINFO_MASKS (loop_vinfo).is_empty ());
/* This is the point where we can re-start analysis with SLP forced off. */
start_over:
/* Now the vectorization factor is final. */
poly_uint64 vectorization_factor = LOOP_VINFO_VECT_FACTOR (loop_vinfo);
gcc_assert (known_ne (vectorization_factor, 0U));
if (LOOP_VINFO_NITERS_KNOWN_P (loop_vinfo) && dump_enabled_p ())
{
dump_printf_loc (MSG_NOTE, vect_location,
"vectorization_factor = ");
dump_dec (MSG_NOTE, vectorization_factor);
dump_printf (MSG_NOTE, ", niters = " HOST_WIDE_INT_PRINT_DEC "\n",
LOOP_VINFO_INT_NITERS (loop_vinfo));
}
HOST_WIDE_INT max_niter
= likely_max_stmt_executions_int (LOOP_VINFO_LOOP (loop_vinfo));
/* Analyze the alignment of the data-refs in the loop.
Fail if a data reference is found that cannot be vectorized. */
ok = vect_analyze_data_refs_alignment (loop_vinfo);
if (!ok)
{
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"bad data alignment.\n");
return false;
}
/* Prune the list of ddrs to be tested at run-time by versioning for alias.
It is important to call pruning after vect_analyze_data_ref_accesses,
since we use grouping information gathered by interleaving analysis. */
ok = vect_prune_runtime_alias_test_list (loop_vinfo);
if (!ok)
return false;
/* Do not invoke vect_enhance_data_refs_alignment for eplilogue
vectorization. */
if (!LOOP_VINFO_EPILOGUE_P (loop_vinfo))
{
/* This pass will decide on using loop versioning and/or loop peeling in
order to enhance the alignment of data references in the loop. */
ok = vect_enhance_data_refs_alignment (loop_vinfo);
if (!ok)
{
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"bad data alignment.\n");
return false;
}
}
if (slp)
{
/* Analyze operations in the SLP instances. Note this may
remove unsupported SLP instances which makes the above
SLP kind detection invalid. */
unsigned old_size = LOOP_VINFO_SLP_INSTANCES (loop_vinfo).length ();
vect_slp_analyze_operations (loop_vinfo);
if (LOOP_VINFO_SLP_INSTANCES (loop_vinfo).length () != old_size)
goto again;
}
/* Scan all the remaining operations in the loop that are not subject
to SLP and make sure they are vectorizable. */
ok = vect_analyze_loop_operations (loop_vinfo);
if (!ok)
{
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"bad operation or unsupported loop bound.\n");
return false;
}
/* Decide whether to use a fully-masked loop for this vectorization
factor. */
LOOP_VINFO_FULLY_MASKED_P (loop_vinfo)
= (LOOP_VINFO_CAN_FULLY_MASK_P (loop_vinfo)
&& vect_verify_full_masking (loop_vinfo));
if (dump_enabled_p ())
{
if (LOOP_VINFO_FULLY_MASKED_P (loop_vinfo))
dump_printf_loc (MSG_NOTE, vect_location,
"using a fully-masked loop.\n");
else
dump_printf_loc (MSG_NOTE, vect_location,
"not using a fully-masked loop.\n");
}
/* If epilog loop is required because of data accesses with gaps,
one additional iteration needs to be peeled. Check if there is
enough iterations for vectorization. */
if (LOOP_VINFO_PEELING_FOR_GAPS (loop_vinfo)
&& LOOP_VINFO_NITERS_KNOWN_P (loop_vinfo)
&& !LOOP_VINFO_FULLY_MASKED_P (loop_vinfo))
{
poly_uint64 vf = LOOP_VINFO_VECT_FACTOR (loop_vinfo);
tree scalar_niters = LOOP_VINFO_NITERSM1 (loop_vinfo);
if (known_lt (wi::to_widest (scalar_niters), vf))
{
if (dump_enabled_p ())
dump_printf_loc (MSG_NOTE, vect_location,
"loop has no enough iterations to support"
" peeling for gaps.\n");
return false;
}
}
/* Check the costings of the loop make vectorizing worthwhile. */
res = vect_analyze_loop_costing (loop_vinfo);
if (res < 0)
goto again;
if (!res)
{
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"Loop costings not worthwhile.\n");
return false;
}
/* Decide whether we need to create an epilogue loop to handle
remaining scalar iterations. */
th = LOOP_VINFO_COST_MODEL_THRESHOLD (loop_vinfo);
unsigned HOST_WIDE_INT const_vf;
if (LOOP_VINFO_FULLY_MASKED_P (loop_vinfo))
/* The main loop handles all iterations. */
LOOP_VINFO_PEELING_FOR_NITER (loop_vinfo) = false;
else if (LOOP_VINFO_NITERS_KNOWN_P (loop_vinfo)
&& LOOP_VINFO_PEELING_FOR_ALIGNMENT (loop_vinfo) > 0)
{
if (!multiple_p (LOOP_VINFO_INT_NITERS (loop_vinfo)
- LOOP_VINFO_PEELING_FOR_ALIGNMENT (loop_vinfo),
LOOP_VINFO_VECT_FACTOR (loop_vinfo)))
LOOP_VINFO_PEELING_FOR_NITER (loop_vinfo) = true;
}
else if (LOOP_VINFO_PEELING_FOR_ALIGNMENT (loop_vinfo)
|| !LOOP_VINFO_VECT_FACTOR (loop_vinfo).is_constant (&const_vf)
|| ((tree_ctz (LOOP_VINFO_NITERS (loop_vinfo))
< (unsigned) exact_log2 (const_vf))
/* In case of versioning, check if the maximum number of
iterations is greater than th. If they are identical,
the epilogue is unnecessary. */
&& (!LOOP_REQUIRES_VERSIONING (loop_vinfo)
|| ((unsigned HOST_WIDE_INT) max_niter
> (th / const_vf) * const_vf))))
LOOP_VINFO_PEELING_FOR_NITER (loop_vinfo) = true;
/* If an epilogue loop is required make sure we can create one. */
if (LOOP_VINFO_PEELING_FOR_GAPS (loop_vinfo)
|| LOOP_VINFO_PEELING_FOR_NITER (loop_vinfo))
{
if (dump_enabled_p ())
dump_printf_loc (MSG_NOTE, vect_location, "epilog loop required\n");
if (!vect_can_advance_ivs_p (loop_vinfo)
|| !slpeel_can_duplicate_loop_p (LOOP_VINFO_LOOP (loop_vinfo),
single_exit (LOOP_VINFO_LOOP
(loop_vinfo))))
{
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"not vectorized: can't create required "
"epilog loop\n");
goto again;
}
}
/* During peeling, we need to check if number of loop iterations is
enough for both peeled prolog loop and vector loop. This check
can be merged along with threshold check of loop versioning, so
increase threshold for this case if necessary. */
if (LOOP_REQUIRES_VERSIONING (loop_vinfo))
{
poly_uint64 niters_th = 0;
if (!vect_use_loop_mask_for_alignment_p (loop_vinfo))
{
/* Niters for peeled prolog loop. */
if (LOOP_VINFO_PEELING_FOR_ALIGNMENT (loop_vinfo) < 0)
{
struct data_reference *dr = LOOP_VINFO_UNALIGNED_DR (loop_vinfo);
tree vectype
= STMT_VINFO_VECTYPE (vinfo_for_stmt (DR_STMT (dr)));
niters_th += TYPE_VECTOR_SUBPARTS (vectype) - 1;
}
else
niters_th += LOOP_VINFO_PEELING_FOR_ALIGNMENT (loop_vinfo);
}
/* Niters for at least one iteration of vectorized loop. */
if (!LOOP_VINFO_FULLY_MASKED_P (loop_vinfo))
niters_th += LOOP_VINFO_VECT_FACTOR (loop_vinfo);
/* One additional iteration because of peeling for gap. */
if (LOOP_VINFO_PEELING_FOR_GAPS (loop_vinfo))
niters_th += 1;
LOOP_VINFO_VERSIONING_THRESHOLD (loop_vinfo) = niters_th;
}
gcc_assert (known_eq (vectorization_factor,
LOOP_VINFO_VECT_FACTOR (loop_vinfo)));
/* Ok to vectorize! */
return true;
again:
/* Try again with SLP forced off but if we didn't do any SLP there is
no point in re-trying. */
if (!slp)
return false;
/* If there are reduction chains re-trying will fail anyway. */
if (! LOOP_VINFO_REDUCTION_CHAINS (loop_vinfo).is_empty ())
return false;
/* Likewise if the grouped loads or stores in the SLP cannot be handled
via interleaving or lane instructions. */
slp_instance instance;
slp_tree node;
unsigned i, j;
FOR_EACH_VEC_ELT (LOOP_VINFO_SLP_INSTANCES (loop_vinfo), i, instance)
{
stmt_vec_info vinfo;
vinfo = vinfo_for_stmt
(SLP_TREE_SCALAR_STMTS (SLP_INSTANCE_TREE (instance))[0]);
if (! STMT_VINFO_GROUPED_ACCESS (vinfo))
continue;
vinfo = vinfo_for_stmt (STMT_VINFO_GROUP_FIRST_ELEMENT (vinfo));
unsigned int size = STMT_VINFO_GROUP_SIZE (vinfo);
tree vectype = STMT_VINFO_VECTYPE (vinfo);
if (! vect_store_lanes_supported (vectype, size, false)
&& ! known_eq (TYPE_VECTOR_SUBPARTS (vectype), 1U)
&& ! vect_grouped_store_supported (vectype, size))
return false;
FOR_EACH_VEC_ELT (SLP_INSTANCE_LOADS (instance), j, node)
{
vinfo = vinfo_for_stmt (SLP_TREE_SCALAR_STMTS (node)[0]);
vinfo = vinfo_for_stmt (STMT_VINFO_GROUP_FIRST_ELEMENT (vinfo));
bool single_element_p = !STMT_VINFO_GROUP_NEXT_ELEMENT (vinfo);
size = STMT_VINFO_GROUP_SIZE (vinfo);
vectype = STMT_VINFO_VECTYPE (vinfo);
if (! vect_load_lanes_supported (vectype, size, false)
&& ! vect_grouped_load_supported (vectype, single_element_p,
size))
return false;
}
}
if (dump_enabled_p ())
dump_printf_loc (MSG_NOTE, vect_location,
"re-trying with SLP disabled\n");
/* Roll back state appropriately. No SLP this time. */
slp = false;
/* Restore vectorization factor as it were without SLP. */
LOOP_VINFO_VECT_FACTOR (loop_vinfo) = saved_vectorization_factor;
/* Free the SLP instances. */
FOR_EACH_VEC_ELT (LOOP_VINFO_SLP_INSTANCES (loop_vinfo), j, instance)
vect_free_slp_instance (instance);
LOOP_VINFO_SLP_INSTANCES (loop_vinfo).release ();
/* Reset SLP type to loop_vect on all stmts. */
for (i = 0; i < LOOP_VINFO_LOOP (loop_vinfo)->num_nodes; ++i)
{
basic_block bb = LOOP_VINFO_BBS (loop_vinfo)[i];
for (gimple_stmt_iterator si = gsi_start_phis (bb);
!gsi_end_p (si); gsi_next (&si))
{
stmt_vec_info stmt_info = vinfo_for_stmt (gsi_stmt (si));
STMT_SLP_TYPE (stmt_info) = loop_vect;
}
for (gimple_stmt_iterator si = gsi_start_bb (bb);
!gsi_end_p (si); gsi_next (&si))
{
stmt_vec_info stmt_info = vinfo_for_stmt (gsi_stmt (si));
STMT_SLP_TYPE (stmt_info) = loop_vect;
if (STMT_VINFO_IN_PATTERN_P (stmt_info))
{
stmt_info = vinfo_for_stmt (STMT_VINFO_RELATED_STMT (stmt_info));
STMT_SLP_TYPE (stmt_info) = loop_vect;
for (gimple_stmt_iterator pi
= gsi_start (STMT_VINFO_PATTERN_DEF_SEQ (stmt_info));
!gsi_end_p (pi); gsi_next (&pi))
{
gimple *pstmt = gsi_stmt (pi);
STMT_SLP_TYPE (vinfo_for_stmt (pstmt)) = loop_vect;
}
}
}
}
/* Free optimized alias test DDRS. */
LOOP_VINFO_LOWER_BOUNDS (loop_vinfo).truncate (0);
LOOP_VINFO_COMP_ALIAS_DDRS (loop_vinfo).release ();
LOOP_VINFO_CHECK_UNEQUAL_ADDRS (loop_vinfo).release ();
/* Reset target cost data. */
destroy_cost_data (LOOP_VINFO_TARGET_COST_DATA (loop_vinfo));
LOOP_VINFO_TARGET_COST_DATA (loop_vinfo)
= init_cost (LOOP_VINFO_LOOP (loop_vinfo));
/* Reset accumulated rgroup information. */
release_vec_loop_masks (&LOOP_VINFO_MASKS (loop_vinfo));
/* Reset assorted flags. */
LOOP_VINFO_PEELING_FOR_NITER (loop_vinfo) = false;
LOOP_VINFO_PEELING_FOR_GAPS (loop_vinfo) = false;
LOOP_VINFO_COST_MODEL_THRESHOLD (loop_vinfo) = 0;
LOOP_VINFO_VERSIONING_THRESHOLD (loop_vinfo) = 0;
LOOP_VINFO_CAN_FULLY_MASK_P (loop_vinfo) = saved_can_fully_mask_p;
goto start_over;
}
/* Function vect_analyze_loop.
Apply a set of analyses on LOOP, and create a loop_vec_info struct
for it. The different analyses will record information in the
loop_vec_info struct. If ORIG_LOOP_VINFO is not NULL epilogue must
be vectorized. */
loop_vec_info
vect_analyze_loop (struct loop *loop, loop_vec_info orig_loop_vinfo)
{
loop_vec_info loop_vinfo;
auto_vector_sizes vector_sizes;
/* Autodetect first vector size we try. */
current_vector_size = 0;
targetm.vectorize.autovectorize_vector_sizes (&vector_sizes);
unsigned int next_size = 0;
if (dump_enabled_p ())
dump_printf_loc (MSG_NOTE, vect_location,
"===== analyze_loop_nest =====\n");
if (loop_outer (loop)
&& loop_vec_info_for_loop (loop_outer (loop))
&& LOOP_VINFO_VECTORIZABLE_P (loop_vec_info_for_loop (loop_outer (loop))))
{
if (dump_enabled_p ())
dump_printf_loc (MSG_NOTE, vect_location,
"outer-loop already vectorized.\n");
return NULL;
}
poly_uint64 autodetected_vector_size = 0;
while (1)
{
/* Check the CFG characteristics of the loop (nesting, entry/exit). */
loop_vinfo = vect_analyze_loop_form (loop);
if (!loop_vinfo)
{
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"bad loop form.\n");
return NULL;
}
bool fatal = false;
if (orig_loop_vinfo)
LOOP_VINFO_ORIG_LOOP_INFO (loop_vinfo) = orig_loop_vinfo;
if (vect_analyze_loop_2 (loop_vinfo, fatal))
{
LOOP_VINFO_VECTORIZABLE_P (loop_vinfo) = 1;
return loop_vinfo;
}
delete loop_vinfo;
if (next_size == 0)
autodetected_vector_size = current_vector_size;
if (next_size < vector_sizes.length ()
&& known_eq (vector_sizes[next_size], autodetected_vector_size))
next_size += 1;
if (fatal
|| next_size == vector_sizes.length ()
|| known_eq (current_vector_size, 0U))
return NULL;
/* Try the next biggest vector size. */
current_vector_size = vector_sizes[next_size++];
if (dump_enabled_p ())
{
dump_printf_loc (MSG_NOTE, vect_location,
"***** Re-trying analysis with "
"vector size ");
dump_dec (MSG_NOTE, current_vector_size);
dump_printf (MSG_NOTE, "\n");
}
}
}
/* Return true if there is an in-order reduction function for CODE, storing
it in *REDUC_FN if so. */
static bool
fold_left_reduction_fn (tree_code code, internal_fn *reduc_fn)
{
switch (code)
{
case PLUS_EXPR:
*reduc_fn = IFN_FOLD_LEFT_PLUS;
return true;
default:
return false;
}
}
/* Function reduction_fn_for_scalar_code
Input:
CODE - tree_code of a reduction operations.
Output:
REDUC_FN - the corresponding internal function to be used to reduce the
vector of partial results into a single scalar result, or IFN_LAST
if the operation is a supported reduction operation, but does not have
such an internal function.
Return FALSE if CODE currently cannot be vectorized as reduction. */
static bool
reduction_fn_for_scalar_code (enum tree_code code, internal_fn *reduc_fn)
{
switch (code)
{
case MAX_EXPR:
*reduc_fn = IFN_REDUC_MAX;
return true;
case MIN_EXPR:
*reduc_fn = IFN_REDUC_MIN;
return true;
case PLUS_EXPR:
*reduc_fn = IFN_REDUC_PLUS;
return true;
case BIT_AND_EXPR:
*reduc_fn = IFN_REDUC_AND;
return true;
case BIT_IOR_EXPR:
*reduc_fn = IFN_REDUC_IOR;
return true;
case BIT_XOR_EXPR:
*reduc_fn = IFN_REDUC_XOR;
return true;
case MULT_EXPR:
case MINUS_EXPR:
*reduc_fn = IFN_LAST;
return true;
default:
return false;
}
}
/* If there is a neutral value X such that SLP reduction NODE would not
be affected by the introduction of additional X elements, return that X,
otherwise return null. CODE is the code of the reduction. REDUC_CHAIN
is true if the SLP statements perform a single reduction, false if each
statement performs an independent reduction. */
static tree
neutral_op_for_slp_reduction (slp_tree slp_node, tree_code code,
bool reduc_chain)
{
vec<gimple *> stmts = SLP_TREE_SCALAR_STMTS (slp_node);
gimple *stmt = stmts[0];
stmt_vec_info stmt_vinfo = vinfo_for_stmt (stmt);
tree vector_type = STMT_VINFO_VECTYPE (stmt_vinfo);
tree scalar_type = TREE_TYPE (vector_type);
struct loop *loop = gimple_bb (stmt)->loop_father;
gcc_assert (loop);
switch (code)
{
case WIDEN_SUM_EXPR:
case DOT_PROD_EXPR:
case SAD_EXPR:
case PLUS_EXPR:
case MINUS_EXPR:
case BIT_IOR_EXPR:
case BIT_XOR_EXPR:
return build_zero_cst (scalar_type);
case MULT_EXPR:
return build_one_cst (scalar_type);
case BIT_AND_EXPR:
return build_all_ones_cst (scalar_type);
case MAX_EXPR:
case MIN_EXPR:
/* For MIN/MAX the initial values are neutral. A reduction chain
has only a single initial value, so that value is neutral for
all statements. */
if (reduc_chain)
return PHI_ARG_DEF_FROM_EDGE (stmt, loop_preheader_edge (loop));
return NULL_TREE;
default:
return NULL_TREE;
}
}
/* Error reporting helper for vect_is_simple_reduction below. GIMPLE statement
STMT is printed with a message MSG. */
static void
report_vect_op (dump_flags_t msg_type, gimple *stmt, const char *msg)
{
dump_printf_loc (msg_type, vect_location, "%s", msg);
dump_gimple_stmt (msg_type, TDF_SLIM, stmt, 0);
}
/* Detect SLP reduction of the form:
#a1 = phi <a5, a0>
a2 = operation (a1)
a3 = operation (a2)
a4 = operation (a3)
a5 = operation (a4)
#a = phi <a5>
PHI is the reduction phi node (#a1 = phi <a5, a0> above)
FIRST_STMT is the first reduction stmt in the chain
(a2 = operation (a1)).
Return TRUE if a reduction chain was detected. */
static bool
vect_is_slp_reduction (loop_vec_info loop_info, gimple *phi,
gimple *first_stmt)
{
struct loop *loop = (gimple_bb (phi))->loop_father;
struct loop *vect_loop = LOOP_VINFO_LOOP (loop_info);
enum tree_code code;
gimple *current_stmt = NULL, *loop_use_stmt = NULL, *first, *next_stmt;
stmt_vec_info use_stmt_info, current_stmt_info;
tree lhs;
imm_use_iterator imm_iter;
use_operand_p use_p;
int nloop_uses, size = 0, n_out_of_loop_uses;
bool found = false;
if (loop != vect_loop)
return false;
lhs = PHI_RESULT (phi);
code = gimple_assign_rhs_code (first_stmt);
while (1)
{
nloop_uses = 0;
n_out_of_loop_uses = 0;
FOR_EACH_IMM_USE_FAST (use_p, imm_iter, lhs)
{
gimple *use_stmt = USE_STMT (use_p);
if (is_gimple_debug (use_stmt))
continue;
/* Check if we got back to the reduction phi. */
if (use_stmt == phi)
{
loop_use_stmt = use_stmt;
found = true;
break;
}
if (flow_bb_inside_loop_p (loop, gimple_bb (use_stmt)))
{
loop_use_stmt = use_stmt;
nloop_uses++;
}
else
n_out_of_loop_uses++;
/* There are can be either a single use in the loop or two uses in
phi nodes. */
if (nloop_uses > 1 || (n_out_of_loop_uses && nloop_uses))
return false;
}
if (found)
break;
/* We reached a statement with no loop uses. */
if (nloop_uses == 0)
return false;
/* This is a loop exit phi, and we haven't reached the reduction phi. */
if (gimple_code (loop_use_stmt) == GIMPLE_PHI)
return false;
if (!is_gimple_assign (loop_use_stmt)
|| code != gimple_assign_rhs_code (loop_use_stmt)
|| !flow_bb_inside_loop_p (loop, gimple_bb (loop_use_stmt)))
return false;
/* Insert USE_STMT into reduction chain. */
use_stmt_info = vinfo_for_stmt (loop_use_stmt);
if (current_stmt)
{
current_stmt_info = vinfo_for_stmt (current_stmt);
GROUP_NEXT_ELEMENT (current_stmt_info) = loop_use_stmt;
GROUP_FIRST_ELEMENT (use_stmt_info)
= GROUP_FIRST_ELEMENT (current_stmt_info);
}
else
GROUP_FIRST_ELEMENT (use_stmt_info) = loop_use_stmt;
lhs = gimple_assign_lhs (loop_use_stmt);
current_stmt = loop_use_stmt;
size++;
}
if (!found || loop_use_stmt != phi || size < 2)
return false;
/* Swap the operands, if needed, to make the reduction operand be the second
operand. */
lhs = PHI_RESULT (phi);
next_stmt = GROUP_FIRST_ELEMENT (vinfo_for_stmt (current_stmt));
while (next_stmt)
{
if (gimple_assign_rhs2 (next_stmt) == lhs)
{
tree op = gimple_assign_rhs1 (next_stmt);
gimple *def_stmt = NULL;
if (TREE_CODE (op) == SSA_NAME)
def_stmt = SSA_NAME_DEF_STMT (op);
/* Check that the other def is either defined in the loop
("vect_internal_def"), or it's an induction (defined by a
loop-header phi-node). */
if (def_stmt
&& gimple_bb (def_stmt)
&& flow_bb_inside_loop_p (loop, gimple_bb (def_stmt))
&& (is_gimple_assign (def_stmt)
|| is_gimple_call (def_stmt)
|| STMT_VINFO_DEF_TYPE (vinfo_for_stmt (def_stmt))
== vect_induction_def
|| (gimple_code (def_stmt) == GIMPLE_PHI
&& STMT_VINFO_DEF_TYPE (vinfo_for_stmt (def_stmt))
== vect_internal_def
&& !is_loop_header_bb_p (gimple_bb (def_stmt)))))
{
lhs = gimple_assign_lhs (next_stmt);
next_stmt = GROUP_NEXT_ELEMENT (vinfo_for_stmt (next_stmt));
continue;
}
return false;
}
else
{
tree op = gimple_assign_rhs2 (next_stmt);
gimple *def_stmt = NULL;
if (TREE_CODE (op) == SSA_NAME)
def_stmt = SSA_NAME_DEF_STMT (op);
/* Check that the other def is either defined in the loop
("vect_internal_def"), or it's an induction (defined by a
loop-header phi-node). */
if (def_stmt
&& gimple_bb (def_stmt)
&& flow_bb_inside_loop_p (loop, gimple_bb (def_stmt))
&& (is_gimple_assign (def_stmt)
|| is_gimple_call (def_stmt)
|| STMT_VINFO_DEF_TYPE (vinfo_for_stmt (def_stmt))
== vect_induction_def
|| (gimple_code (def_stmt) == GIMPLE_PHI
&& STMT_VINFO_DEF_TYPE (vinfo_for_stmt (def_stmt))
== vect_internal_def
&& !is_loop_header_bb_p (gimple_bb (def_stmt)))))
{
if (dump_enabled_p ())
{
dump_printf_loc (MSG_NOTE, vect_location, "swapping oprnds: ");
dump_gimple_stmt (MSG_NOTE, TDF_SLIM, next_stmt, 0);
}
swap_ssa_operands (next_stmt,
gimple_assign_rhs1_ptr (next_stmt),
gimple_assign_rhs2_ptr (next_stmt));
update_stmt (next_stmt);
if (CONSTANT_CLASS_P (gimple_assign_rhs1 (next_stmt)))
LOOP_VINFO_OPERANDS_SWAPPED (loop_info) = true;
}
else
return false;
}
lhs = gimple_assign_lhs (next_stmt);
next_stmt = GROUP_NEXT_ELEMENT (vinfo_for_stmt (next_stmt));
}
/* Save the chain for further analysis in SLP detection. */
first = GROUP_FIRST_ELEMENT (vinfo_for_stmt (current_stmt));
LOOP_VINFO_REDUCTION_CHAINS (loop_info).safe_push (first);
GROUP_SIZE (vinfo_for_stmt (first)) = size;
return true;
}
/* Return true if we need an in-order reduction for operation CODE
on type TYPE. NEED_WRAPPING_INTEGRAL_OVERFLOW is true if integer
overflow must wrap. */
static bool
needs_fold_left_reduction_p (tree type, tree_code code,
bool need_wrapping_integral_overflow)
{
/* CHECKME: check for !flag_finite_math_only too? */
if (SCALAR_FLOAT_TYPE_P (type))
switch (code)
{
case MIN_EXPR:
case MAX_EXPR:
return false;
default:
return !flag_associative_math;
}
if (INTEGRAL_TYPE_P (type))
{
if (!operation_no_trapping_overflow (type, code))
return true;
if (need_wrapping_integral_overflow
&& !TYPE_OVERFLOW_WRAPS (type)
&& operation_can_overflow (code))
return true;
return false;
}
if (SAT_FIXED_POINT_TYPE_P (type))
return true;
return false;
}
/* Return true if the reduction PHI in LOOP with latch arg LOOP_ARG and
reduction operation CODE has a handled computation expression. */
bool
check_reduction_path (location_t loc, loop_p loop, gphi *phi, tree loop_arg,
enum tree_code code)
{
auto_vec<std::pair<ssa_op_iter, use_operand_p> > path;
auto_bitmap visited;
tree lookfor = PHI_RESULT (phi);
ssa_op_iter curri;
use_operand_p curr = op_iter_init_phiuse (&curri, phi, SSA_OP_USE);
while (USE_FROM_PTR (curr) != loop_arg)
curr = op_iter_next_use (&curri);
curri.i = curri.numops;
do
{
path.safe_push (std::make_pair (curri, curr));
tree use = USE_FROM_PTR (curr);
if (use == lookfor)
break;
gimple *def = SSA_NAME_DEF_STMT (use);
if (gimple_nop_p (def)
|| ! flow_bb_inside_loop_p (loop, gimple_bb (def)))
{
pop:
do
{
std::pair<ssa_op_iter, use_operand_p> x = path.pop ();
curri = x.first;
curr = x.second;
do
curr = op_iter_next_use (&curri);
/* Skip already visited or non-SSA operands (from iterating
over PHI args). */
while (curr != NULL_USE_OPERAND_P
&& (TREE_CODE (USE_FROM_PTR (curr)) != SSA_NAME
|| ! bitmap_set_bit (visited,
SSA_NAME_VERSION
(USE_FROM_PTR (curr)))));
}
while (curr == NULL_USE_OPERAND_P && ! path.is_empty ());
if (curr == NULL_USE_OPERAND_P)
break;
}
else
{
if (gimple_code (def) == GIMPLE_PHI)
curr = op_iter_init_phiuse (&curri, as_a <gphi *>(def), SSA_OP_USE);
else
curr = op_iter_init_use (&curri, def, SSA_OP_USE);
while (curr != NULL_USE_OPERAND_P
&& (TREE_CODE (USE_FROM_PTR (curr)) != SSA_NAME
|| ! bitmap_set_bit (visited,
SSA_NAME_VERSION
(USE_FROM_PTR (curr)))))
curr = op_iter_next_use (&curri);
if (curr == NULL_USE_OPERAND_P)
goto pop;
}
}
while (1);
if (dump_file && (dump_flags & TDF_DETAILS))
{
dump_printf_loc (MSG_NOTE, loc, "reduction path: ");
unsigned i;
std::pair<ssa_op_iter, use_operand_p> *x;
FOR_EACH_VEC_ELT (path, i, x)
{
dump_generic_expr (MSG_NOTE, TDF_SLIM, USE_FROM_PTR (x->second));
dump_printf (MSG_NOTE, " ");
}
dump_printf (MSG_NOTE, "\n");
}
/* Check whether the reduction path detected is valid. */
bool fail = path.length () == 0;
bool neg = false;
for (unsigned i = 1; i < path.length (); ++i)
{
gimple *use_stmt = USE_STMT (path[i].second);
tree op = USE_FROM_PTR (path[i].second);
if (! has_single_use (op)
|| ! is_gimple_assign (use_stmt))
{
fail = true;
break;
}
if (gimple_assign_rhs_code (use_stmt) != code)
{
if (code == PLUS_EXPR
&& gimple_assign_rhs_code (use_stmt) == MINUS_EXPR)
{
/* Track whether we negate the reduction value each iteration. */
if (gimple_assign_rhs2 (use_stmt) == op)
neg = ! neg;
}
else
{
fail = true;
break;
}
}
}
return ! fail && ! neg;
}
/* Function vect_is_simple_reduction
(1) Detect a cross-iteration def-use cycle that represents a simple
reduction computation. We look for the following pattern:
loop_header:
a1 = phi < a0, a2 >
a3 = ...
a2 = operation (a3, a1)
or
a3 = ...
loop_header:
a1 = phi < a0, a2 >
a2 = operation (a3, a1)
such that:
1. operation is commutative and associative and it is safe to
change the order of the computation
2. no uses for a2 in the loop (a2 is used out of the loop)
3. no uses of a1 in the loop besides the reduction operation
4. no uses of a1 outside the loop.
Conditions 1,4 are tested here.
Conditions 2,3 are tested in vect_mark_stmts_to_be_vectorized.
(2) Detect a cross-iteration def-use cycle in nested loops, i.e.,
nested cycles.
(3) Detect cycles of phi nodes in outer-loop vectorization, i.e., double
reductions:
a1 = phi < a0, a2 >
inner loop (def of a3)
a2 = phi < a3 >
(4) Detect condition expressions, ie:
for (int i = 0; i < N; i++)
if (a[i] < val)
ret_val = a[i];
*/
static gimple *
vect_is_simple_reduction (loop_vec_info loop_info, gimple *phi,
bool *double_reduc,
bool need_wrapping_integral_overflow,
enum vect_reduction_type *v_reduc_type)
{
struct loop *loop = (gimple_bb (phi))->loop_father;
struct loop *vect_loop = LOOP_VINFO_LOOP (loop_info);
gimple *def_stmt, *def1 = NULL, *def2 = NULL, *phi_use_stmt = NULL;
enum tree_code orig_code, code;
tree op1, op2, op3 = NULL_TREE, op4 = NULL_TREE;
tree type;
int nloop_uses;
tree name;
imm_use_iterator imm_iter;
use_operand_p use_p;
bool phi_def;
*double_reduc = false;
*v_reduc_type = TREE_CODE_REDUCTION;
tree phi_name = PHI_RESULT (phi);
/* ??? If there are no uses of the PHI result the inner loop reduction
won't be detected as possibly double-reduction by vectorizable_reduction
because that tries to walk the PHI arg from the preheader edge which
can be constant. See PR60382. */
if (has_zero_uses (phi_name))
return NULL;
nloop_uses = 0;
FOR_EACH_IMM_USE_FAST (use_p, imm_iter, phi_name)
{
gimple *use_stmt = USE_STMT (use_p);
if (is_gimple_debug (use_stmt))
continue;
if (!flow_bb_inside_loop_p (loop, gimple_bb (use_stmt)))
{
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"intermediate value used outside loop.\n");
return NULL;
}
nloop_uses++;
if (nloop_uses > 1)
{
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"reduction value used in loop.\n");
return NULL;
}
phi_use_stmt = use_stmt;
}
edge latch_e = loop_latch_edge (loop);
tree loop_arg = PHI_ARG_DEF_FROM_EDGE (phi, latch_e);
if (TREE_CODE (loop_arg) != SSA_NAME)
{
if (dump_enabled_p ())
{
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"reduction: not ssa_name: ");
dump_generic_expr (MSG_MISSED_OPTIMIZATION, TDF_SLIM, loop_arg);
dump_printf (MSG_MISSED_OPTIMIZATION, "\n");
}
return NULL;
}
def_stmt = SSA_NAME_DEF_STMT (loop_arg);
if (is_gimple_assign (def_stmt))
{
name = gimple_assign_lhs (def_stmt);
phi_def = false;
}
else if (gimple_code (def_stmt) == GIMPLE_PHI)
{
name = PHI_RESULT (def_stmt);
phi_def = true;
}
else
{
if (dump_enabled_p ())
{
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"reduction: unhandled reduction operation: ");
dump_gimple_stmt (MSG_MISSED_OPTIMIZATION, TDF_SLIM, def_stmt, 0);
}
return NULL;
}
if (! flow_bb_inside_loop_p (loop, gimple_bb (def_stmt)))
return NULL;
nloop_uses = 0;
auto_vec<gphi *, 3> lcphis;
FOR_EACH_IMM_USE_FAST (use_p, imm_iter, name)
{
gimple *use_stmt = USE_STMT (use_p);
if (is_gimple_debug (use_stmt))
continue;
if (flow_bb_inside_loop_p (loop, gimple_bb (use_stmt)))
nloop_uses++;
else
/* We can have more than one loop-closed PHI. */
lcphis.safe_push (as_a <gphi *> (use_stmt));
if (nloop_uses > 1)
{
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"reduction used in loop.\n");
return NULL;
}
}
/* If DEF_STMT is a phi node itself, we expect it to have a single argument
defined in the inner loop. */
if (phi_def)
{
op1 = PHI_ARG_DEF (def_stmt, 0);
if (gimple_phi_num_args (def_stmt) != 1
|| TREE_CODE (op1) != SSA_NAME)
{
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"unsupported phi node definition.\n");
return NULL;
}
def1 = SSA_NAME_DEF_STMT (op1);
if (gimple_bb (def1)
&& flow_bb_inside_loop_p (loop, gimple_bb (def_stmt))
&& loop->inner
&& flow_bb_inside_loop_p (loop->inner, gimple_bb (def1))
&& is_gimple_assign (def1)
&& flow_bb_inside_loop_p (loop->inner, gimple_bb (phi_use_stmt)))
{
if (dump_enabled_p ())
report_vect_op (MSG_NOTE, def_stmt,
"detected double reduction: ");
*double_reduc = true;
return def_stmt;
}
return NULL;
}
/* If we are vectorizing an inner reduction we are executing that
in the original order only in case we are not dealing with a
double reduction. */
bool check_reduction = true;
if (flow_loop_nested_p (vect_loop, loop))
{
gphi *lcphi;
unsigned i;
check_reduction = false;
FOR_EACH_VEC_ELT (lcphis, i, lcphi)
FOR_EACH_IMM_USE_FAST (use_p, imm_iter, gimple_phi_result (lcphi))
{
gimple *use_stmt = USE_STMT (use_p);
if (is_gimple_debug (use_stmt))
continue;
if (! flow_bb_inside_loop_p (vect_loop, gimple_bb (use_stmt)))
check_reduction = true;
}
}
bool nested_in_vect_loop = flow_loop_nested_p (vect_loop, loop);
code = orig_code = gimple_assign_rhs_code (def_stmt);
/* We can handle "res -= x[i]", which is non-associative by
simply rewriting this into "res += -x[i]". Avoid changing
gimple instruction for the first simple tests and only do this
if we're allowed to change code at all. */
if (code == MINUS_EXPR && gimple_assign_rhs2 (def_stmt) != phi_name)
code = PLUS_EXPR;
if (code == COND_EXPR)
{
if (! nested_in_vect_loop)
*v_reduc_type = COND_REDUCTION;
op3 = gimple_assign_rhs1 (def_stmt);
if (COMPARISON_CLASS_P (op3))
{
op4 = TREE_OPERAND (op3, 1);
op3 = TREE_OPERAND (op3, 0);
}
if (op3 == phi_name || op4 == phi_name)
{
if (dump_enabled_p ())
report_vect_op (MSG_MISSED_OPTIMIZATION, def_stmt,
"reduction: condition depends on previous"
" iteration: ");
return NULL;
}
op1 = gimple_assign_rhs2 (def_stmt);
op2 = gimple_assign_rhs3 (def_stmt);
}
else if (!commutative_tree_code (code) || !associative_tree_code (code))
{
if (dump_enabled_p ())
report_vect_op (MSG_MISSED_OPTIMIZATION, def_stmt,
"reduction: not commutative/associative: ");
return NULL;
}
else if (get_gimple_rhs_class (code) == GIMPLE_BINARY_RHS)
{
op1 = gimple_assign_rhs1 (def_stmt);
op2 = gimple_assign_rhs2 (def_stmt);
}
else
{
if (dump_enabled_p ())
report_vect_op (MSG_MISSED_OPTIMIZATION, def_stmt,
"reduction: not handled operation: ");
return NULL;
}
if (TREE_CODE (op1) != SSA_NAME && TREE_CODE (op2) != SSA_NAME)
{
if (dump_enabled_p ())
report_vect_op (MSG_MISSED_OPTIMIZATION, def_stmt,
"reduction: both uses not ssa_names: ");
return NULL;
}
type = TREE_TYPE (gimple_assign_lhs (def_stmt));
if ((TREE_CODE (op1) == SSA_NAME
&& !types_compatible_p (type,TREE_TYPE (op1)))
|| (TREE_CODE (op2) == SSA_NAME
&& !types_compatible_p (type, TREE_TYPE (op2)))
|| (op3 && TREE_CODE (op3) == SSA_NAME
&& !types_compatible_p (type, TREE_TYPE (op3)))
|| (op4 && TREE_CODE (op4) == SSA_NAME
&& !types_compatible_p (type, TREE_TYPE (op4))))
{
if (dump_enabled_p ())
{
dump_printf_loc (MSG_NOTE, vect_location,
"reduction: multiple types: operation type: ");
dump_generic_expr (MSG_NOTE, TDF_SLIM, type);
dump_printf (MSG_NOTE, ", operands types: ");
dump_generic_expr (MSG_NOTE, TDF_SLIM,
TREE_TYPE (op1));
dump_printf (MSG_NOTE, ",");
dump_generic_expr (MSG_NOTE, TDF_SLIM,
TREE_TYPE (op2));
if (op3)
{
dump_printf (MSG_NOTE, ",");
dump_generic_expr (MSG_NOTE, TDF_SLIM,
TREE_TYPE (op3));
}
if (op4)
{
dump_printf (MSG_NOTE, ",");
dump_generic_expr (MSG_NOTE, TDF_SLIM,
TREE_TYPE (op4));
}
dump_printf (MSG_NOTE, "\n");
}
return NULL;
}
/* Check whether it's ok to change the order of the computation.
Generally, when vectorizing a reduction we change the order of the
computation. This may change the behavior of the program in some
cases, so we need to check that this is ok. One exception is when
vectorizing an outer-loop: the inner-loop is executed sequentially,
and therefore vectorizing reductions in the inner-loop during
outer-loop vectorization is safe. */
if (check_reduction
&& *v_reduc_type == TREE_CODE_REDUCTION
&& needs_fold_left_reduction_p (type, code,
need_wrapping_integral_overflow))
*v_reduc_type = FOLD_LEFT_REDUCTION;
/* Reduction is safe. We're dealing with one of the following:
1) integer arithmetic and no trapv
2) floating point arithmetic, and special flags permit this optimization
3) nested cycle (i.e., outer loop vectorization). */
if (TREE_CODE (op1) == SSA_NAME)
def1 = SSA_NAME_DEF_STMT (op1);
if (TREE_CODE (op2) == SSA_NAME)
def2 = SSA_NAME_DEF_STMT (op2);
if (code != COND_EXPR
&& ((!def1 || gimple_nop_p (def1)) && (!def2 || gimple_nop_p (def2))))
{
if (dump_enabled_p ())
report_vect_op (MSG_NOTE, def_stmt, "reduction: no defs for operands: ");
return NULL;
}
/* Check that one def is the reduction def, defined by PHI,
the other def is either defined in the loop ("vect_internal_def"),
or it's an induction (defined by a loop-header phi-node). */
if (def2 && def2 == phi
&& (code == COND_EXPR
|| !def1 || gimple_nop_p (def1)
|| !flow_bb_inside_loop_p (loop, gimple_bb (def1))
|| (def1 && flow_bb_inside_loop_p (loop, gimple_bb (def1))
&& (is_gimple_assign (def1)
|| is_gimple_call (def1)
|| STMT_VINFO_DEF_TYPE (vinfo_for_stmt (def1))
== vect_induction_def
|| (gimple_code (def1) == GIMPLE_PHI
&& STMT_VINFO_DEF_TYPE (vinfo_for_stmt (def1))
== vect_internal_def
&& !is_loop_header_bb_p (gimple_bb (def1)))))))
{
if (dump_enabled_p ())
report_vect_op (MSG_NOTE, def_stmt, "detected reduction: ");
return def_stmt;
}
if (def1 && def1 == phi
&& (code == COND_EXPR
|| !def2 || gimple_nop_p (def2)
|| !flow_bb_inside_loop_p (loop, gimple_bb (def2))
|| (def2 && flow_bb_inside_loop_p (loop, gimple_bb (def2))
&& (is_gimple_assign (def2)
|| is_gimple_call (def2)
|| STMT_VINFO_DEF_TYPE (vinfo_for_stmt (def2))
== vect_induction_def
|| (gimple_code (def2) == GIMPLE_PHI
&& STMT_VINFO_DEF_TYPE (vinfo_for_stmt (def2))
== vect_internal_def
&& !is_loop_header_bb_p (gimple_bb (def2)))))))
{
if (! nested_in_vect_loop && orig_code != MINUS_EXPR)
{
/* Check if we can swap operands (just for simplicity - so that
the rest of the code can assume that the reduction variable
is always the last (second) argument). */
if (code == COND_EXPR)
{
/* Swap cond_expr by inverting the condition. */
tree cond_expr = gimple_assign_rhs1 (def_stmt);
enum tree_code invert_code = ERROR_MARK;
enum tree_code cond_code = TREE_CODE (cond_expr);
if (TREE_CODE_CLASS (cond_code) == tcc_comparison)
{
bool honor_nans = HONOR_NANS (TREE_OPERAND (cond_expr, 0));
invert_code = invert_tree_comparison (cond_code, honor_nans);
}
if (invert_code != ERROR_MARK)
{
TREE_SET_CODE (cond_expr, invert_code);
swap_ssa_operands (def_stmt,
gimple_assign_rhs2_ptr (def_stmt),
gimple_assign_rhs3_ptr (def_stmt));
}
else
{
if (dump_enabled_p ())
report_vect_op (MSG_NOTE, def_stmt,
"detected reduction: cannot swap operands "
"for cond_expr");
return NULL;
}
}
else
swap_ssa_operands (def_stmt, gimple_assign_rhs1_ptr (def_stmt),
gimple_assign_rhs2_ptr (def_stmt));
if (dump_enabled_p ())
report_vect_op (MSG_NOTE, def_stmt,
"detected reduction: need to swap operands: ");
if (CONSTANT_CLASS_P (gimple_assign_rhs1 (def_stmt)))
LOOP_VINFO_OPERANDS_SWAPPED (loop_info) = true;
}
else
{
if (dump_enabled_p ())
report_vect_op (MSG_NOTE, def_stmt, "detected reduction: ");
}
return def_stmt;
}
/* Try to find SLP reduction chain. */
if (! nested_in_vect_loop
&& code != COND_EXPR
&& orig_code != MINUS_EXPR
&& vect_is_slp_reduction (loop_info, phi, def_stmt))
{
if (dump_enabled_p ())
report_vect_op (MSG_NOTE, def_stmt,
"reduction: detected reduction chain: ");
return def_stmt;
}
/* Dissolve group eventually half-built by vect_is_slp_reduction. */
gimple *first = GROUP_FIRST_ELEMENT (vinfo_for_stmt (def_stmt));
while (first)
{
gimple *next = GROUP_NEXT_ELEMENT (vinfo_for_stmt (first));
GROUP_FIRST_ELEMENT (vinfo_for_stmt (first)) = NULL;
GROUP_NEXT_ELEMENT (vinfo_for_stmt (first)) = NULL;
first = next;
}
/* Look for the expression computing loop_arg from loop PHI result. */
if (check_reduction_path (vect_location, loop, as_a <gphi *> (phi), loop_arg,
code))
return def_stmt;
if (dump_enabled_p ())
{
report_vect_op (MSG_MISSED_OPTIMIZATION, def_stmt,
"reduction: unknown pattern: ");
}
return NULL;
}
/* Wrapper around vect_is_simple_reduction, which will modify code
in-place if it enables detection of more reductions. Arguments
as there. */
gimple *
vect_force_simple_reduction (loop_vec_info loop_info, gimple *phi,
bool *double_reduc,
bool need_wrapping_integral_overflow)
{
enum vect_reduction_type v_reduc_type;
gimple *def = vect_is_simple_reduction (loop_info, phi, double_reduc,
need_wrapping_integral_overflow,
&v_reduc_type);
if (def)
{
stmt_vec_info reduc_def_info = vinfo_for_stmt (phi);
STMT_VINFO_REDUC_TYPE (reduc_def_info) = v_reduc_type;
STMT_VINFO_REDUC_DEF (reduc_def_info) = def;
reduc_def_info = vinfo_for_stmt (def);
STMT_VINFO_REDUC_TYPE (reduc_def_info) = v_reduc_type;
STMT_VINFO_REDUC_DEF (reduc_def_info) = phi;
}
return def;
}
/* Calculate cost of peeling the loop PEEL_ITERS_PROLOGUE times. */
int
vect_get_known_peeling_cost (loop_vec_info loop_vinfo, int peel_iters_prologue,
int *peel_iters_epilogue,
stmt_vector_for_cost *scalar_cost_vec,
stmt_vector_for_cost *prologue_cost_vec,
stmt_vector_for_cost *epilogue_cost_vec)
{
int retval = 0;
int assumed_vf = vect_vf_for_cost (loop_vinfo);
if (!LOOP_VINFO_NITERS_KNOWN_P (loop_vinfo))
{
*peel_iters_epilogue = assumed_vf / 2;
if (dump_enabled_p ())
dump_printf_loc (MSG_NOTE, vect_location,
"cost model: epilogue peel iters set to vf/2 "
"because loop iterations are unknown .\n");
/* If peeled iterations are known but number of scalar loop
iterations are unknown, count a taken branch per peeled loop. */
retval = record_stmt_cost (prologue_cost_vec, 1, cond_branch_taken,
NULL, 0, vect_prologue);
retval = record_stmt_cost (prologue_cost_vec, 1, cond_branch_taken,
NULL, 0, vect_epilogue);
}
else
{
int niters = LOOP_VINFO_INT_NITERS (loop_vinfo);
peel_iters_prologue = niters < peel_iters_prologue ?
niters : peel_iters_prologue;
*peel_iters_epilogue = (niters - peel_iters_prologue) % assumed_vf;
/* If we need to peel for gaps, but no peeling is required, we have to
peel VF iterations. */
if (LOOP_VINFO_PEELING_FOR_GAPS (loop_vinfo) && !*peel_iters_epilogue)
*peel_iters_epilogue = assumed_vf;
}
stmt_info_for_cost *si;
int j;
if (peel_iters_prologue)
FOR_EACH_VEC_ELT (*scalar_cost_vec, j, si)
{
stmt_vec_info stmt_info
= si->stmt ? vinfo_for_stmt (si->stmt) : NULL;
retval += record_stmt_cost (prologue_cost_vec,
si->count * peel_iters_prologue,
si->kind, stmt_info, si->misalign,
vect_prologue);
}
if (*peel_iters_epilogue)
FOR_EACH_VEC_ELT (*scalar_cost_vec, j, si)
{
stmt_vec_info stmt_info
= si->stmt ? vinfo_for_stmt (si->stmt) : NULL;
retval += record_stmt_cost (epilogue_cost_vec,
si->count * *peel_iters_epilogue,
si->kind, stmt_info, si->misalign,
vect_epilogue);
}
return retval;
}
/* Function vect_estimate_min_profitable_iters
Return the number of iterations required for the vector version of the
loop to be profitable relative to the cost of the scalar version of the
loop.
*RET_MIN_PROFITABLE_NITERS is a cost model profitability threshold
of iterations for vectorization. -1 value means loop vectorization
is not profitable. This returned value may be used for dynamic
profitability check.
*RET_MIN_PROFITABLE_ESTIMATE is a profitability threshold to be used
for static check against estimated number of iterations. */
static void
vect_estimate_min_profitable_iters (loop_vec_info loop_vinfo,
int *ret_min_profitable_niters,
int *ret_min_profitable_estimate)
{
int min_profitable_iters;
int min_profitable_estimate;
int peel_iters_prologue;
int peel_iters_epilogue;
unsigned vec_inside_cost = 0;
int vec_outside_cost = 0;
unsigned vec_prologue_cost = 0;
unsigned vec_epilogue_cost = 0;
int scalar_single_iter_cost = 0;
int scalar_outside_cost = 0;
int assumed_vf = vect_vf_for_cost (loop_vinfo);
int npeel = LOOP_VINFO_PEELING_FOR_ALIGNMENT (loop_vinfo);
void *target_cost_data = LOOP_VINFO_TARGET_COST_DATA (loop_vinfo);
/* Cost model disabled. */
if (unlimited_cost_model (LOOP_VINFO_LOOP (loop_vinfo)))
{
dump_printf_loc (MSG_NOTE, vect_location, "cost model disabled.\n");
*ret_min_profitable_niters = 0;
*ret_min_profitable_estimate = 0;
return;
}
/* Requires loop versioning tests to handle misalignment. */
if (LOOP_REQUIRES_VERSIONING_FOR_ALIGNMENT (loop_vinfo))
{
/* FIXME: Make cost depend on complexity of individual check. */
unsigned len = LOOP_VINFO_MAY_MISALIGN_STMTS (loop_vinfo).length ();
(void) add_stmt_cost (target_cost_data, len, vector_stmt, NULL, 0,
vect_prologue);
dump_printf (MSG_NOTE,
"cost model: Adding cost of checks for loop "
"versioning to treat misalignment.\n");
}
/* Requires loop versioning with alias checks. */
if (LOOP_REQUIRES_VERSIONING_FOR_ALIAS (loop_vinfo))
{
/* FIXME: Make cost depend on complexity of individual check. */
unsigned len = LOOP_VINFO_COMP_ALIAS_DDRS (loop_vinfo).length ();
(void) add_stmt_cost (target_cost_data, len, vector_stmt, NULL, 0,
vect_prologue);
len = LOOP_VINFO_CHECK_UNEQUAL_ADDRS (loop_vinfo).length ();
if (len)
/* Count LEN - 1 ANDs and LEN comparisons. */
(void) add_stmt_cost (target_cost_data, len * 2 - 1, scalar_stmt,
NULL, 0, vect_prologue);
len = LOOP_VINFO_LOWER_BOUNDS (loop_vinfo).length ();
if (len)
{
/* Count LEN - 1 ANDs and LEN comparisons. */
unsigned int nstmts = len * 2 - 1;
/* +1 for each bias that needs adding. */
for (unsigned int i = 0; i < len; ++i)
if (!LOOP_VINFO_LOWER_BOUNDS (loop_vinfo)[i].unsigned_p)
nstmts += 1;
(void) add_stmt_cost (target_cost_data, nstmts, scalar_stmt,
NULL, 0, vect_prologue);
}
dump_printf (MSG_NOTE,
"cost model: Adding cost of checks for loop "
"versioning aliasing.\n");
}
/* Requires loop versioning with niter checks. */
if (LOOP_REQUIRES_VERSIONING_FOR_NITERS (loop_vinfo))
{
/* FIXME: Make cost depend on complexity of individual check. */
(void) add_stmt_cost (target_cost_data, 1, vector_stmt, NULL, 0,
vect_prologue);
dump_printf (MSG_NOTE,
"cost model: Adding cost of checks for loop "
"versioning niters.\n");
}
if (LOOP_REQUIRES_VERSIONING (loop_vinfo))
(void) add_stmt_cost (target_cost_data, 1, cond_branch_taken, NULL, 0,
vect_prologue);
/* Count statements in scalar loop. Using this as scalar cost for a single
iteration for now.
TODO: Add outer loop support.
TODO: Consider assigning different costs to different scalar
statements. */
scalar_single_iter_cost
= LOOP_VINFO_SINGLE_SCALAR_ITERATION_COST (loop_vinfo);
/* Add additional cost for the peeled instructions in prologue and epilogue
loop. (For fully-masked loops there will be no peeling.)
FORNOW: If we don't know the value of peel_iters for prologue or epilogue
at compile-time - we assume it's vf/2 (the worst would be vf-1).
TODO: Build an expression that represents peel_iters for prologue and
epilogue to be used in a run-time test. */
if (LOOP_VINFO_FULLY_MASKED_P (loop_vinfo))
{
peel_iters_prologue = 0;
peel_iters_epilogue = 0;
if (LOOP_VINFO_PEELING_FOR_GAPS (loop_vinfo))
{
/* We need to peel exactly one iteration. */
peel_iters_epilogue += 1;
stmt_info_for_cost *si;
int j;
FOR_EACH_VEC_ELT (LOOP_VINFO_SCALAR_ITERATION_COST (loop_vinfo),
j, si)
{
struct _stmt_vec_info *stmt_info
= si->stmt ? vinfo_for_stmt (si->stmt) : NULL;
(void) add_stmt_cost (target_cost_data, si->count,
si->kind, stmt_info, si->misalign,
vect_epilogue);
}
}
}
else if (npeel < 0)
{
peel_iters_prologue = assumed_vf / 2;
dump_printf (MSG_NOTE, "cost model: "
"prologue peel iters set to vf/2.\n");
/* If peeling for alignment is unknown, loop bound of main loop becomes
unknown. */
peel_iters_epilogue = assumed_vf / 2;
dump_printf (MSG_NOTE, "cost model: "
"epilogue peel iters set to vf/2 because "
"peeling for alignment is unknown.\n");
/* If peeled iterations are unknown, count a taken branch and a not taken
branch per peeled loop. Even if scalar loop iterations are known,
vector iterations are not known since peeled prologue iterations are
not known. Hence guards remain the same. */
(void) add_stmt_cost (target_cost_data, 1, cond_branch_taken,
NULL, 0, vect_prologue);
(void) add_stmt_cost (target_cost_data, 1, cond_branch_not_taken,
NULL, 0, vect_prologue);
(void) add_stmt_cost (target_cost_data, 1, cond_branch_taken,
NULL, 0, vect_epilogue);
(void) add_stmt_cost (target_cost_data, 1, cond_branch_not_taken,
NULL, 0, vect_epilogue);
stmt_info_for_cost *si;
int j;
FOR_EACH_VEC_ELT (LOOP_VINFO_SCALAR_ITERATION_COST (loop_vinfo), j, si)
{
struct _stmt_vec_info *stmt_info
= si->stmt ? vinfo_for_stmt (si->stmt) : NULL;
(void) add_stmt_cost (target_cost_data,
si->count * peel_iters_prologue,
si->kind, stmt_info, si->misalign,
vect_prologue);
(void) add_stmt_cost (target_cost_data,
si->count * peel_iters_epilogue,
si->kind, stmt_info, si->misalign,
vect_epilogue);
}
}
else
{
stmt_vector_for_cost prologue_cost_vec, epilogue_cost_vec;
stmt_info_for_cost *si;
int j;
void *data = LOOP_VINFO_TARGET_COST_DATA (loop_vinfo);
prologue_cost_vec.create (2);
epilogue_cost_vec.create (2);
peel_iters_prologue = npeel;
(void) vect_get_known_peeling_cost (loop_vinfo, peel_iters_prologue,
&peel_iters_epilogue,
&LOOP_VINFO_SCALAR_ITERATION_COST
(loop_vinfo),
&prologue_cost_vec,
&epilogue_cost_vec);
FOR_EACH_VEC_ELT (prologue_cost_vec, j, si)
{
struct _stmt_vec_info *stmt_info
= si->stmt ? vinfo_for_stmt (si->stmt) : NULL;
(void) add_stmt_cost (data, si->count, si->kind, stmt_info,
si->misalign, vect_prologue);
}
FOR_EACH_VEC_ELT (epilogue_cost_vec, j, si)
{
struct _stmt_vec_info *stmt_info
= si->stmt ? vinfo_for_stmt (si->stmt) : NULL;
(void) add_stmt_cost (data, si->count, si->kind, stmt_info,
si->misalign, vect_epilogue);
}
prologue_cost_vec.release ();
epilogue_cost_vec.release ();
}
/* FORNOW: The scalar outside cost is incremented in one of the
following ways:
1. The vectorizer checks for alignment and aliasing and generates
a condition that allows dynamic vectorization. A cost model
check is ANDED with the versioning condition. Hence scalar code
path now has the added cost of the versioning check.
if (cost > th & versioning_check)
jmp to vector code
Hence run-time scalar is incremented by not-taken branch cost.
2. The vectorizer then checks if a prologue is required. If the
cost model check was not done before during versioning, it has to
be done before the prologue check.
if (cost <= th)
prologue = scalar_iters
if (prologue == 0)
jmp to vector code
else
execute prologue
if (prologue == num_iters)
go to exit
Hence the run-time scalar cost is incremented by a taken branch,
plus a not-taken branch, plus a taken branch cost.
3. The vectorizer then checks if an epilogue is required. If the
cost model check was not done before during prologue check, it
has to be done with the epilogue check.
if (prologue == 0)
jmp to vector code
else
execute prologue
if (prologue == num_iters)
go to exit
vector code:
if ((cost <= th) | (scalar_iters-prologue-epilogue == 0))
jmp to epilogue
Hence the run-time scalar cost should be incremented by 2 taken
branches.
TODO: The back end may reorder the BBS's differently and reverse
conditions/branch directions. Change the estimates below to
something more reasonable. */
/* If the number of iterations is known and we do not do versioning, we can
decide whether to vectorize at compile time. Hence the scalar version
do not carry cost model guard costs. */
if (!LOOP_VINFO_NITERS_KNOWN_P (loop_vinfo)
|| LOOP_REQUIRES_VERSIONING (loop_vinfo))
{
/* Cost model check occurs at versioning. */
if (LOOP_REQUIRES_VERSIONING (loop_vinfo))
scalar_outside_cost += vect_get_stmt_cost (cond_branch_not_taken);
else
{
/* Cost model check occurs at prologue generation. */
if (LOOP_VINFO_PEELING_FOR_ALIGNMENT (loop_vinfo) < 0)
scalar_outside_cost += 2 * vect_get_stmt_cost (cond_branch_taken)
+ vect_get_stmt_cost (cond_branch_not_taken);
/* Cost model check occurs at epilogue generation. */
else
scalar_outside_cost += 2 * vect_get_stmt_cost (cond_branch_taken);
}
}
/* Complete the target-specific cost calculations. */
finish_cost (LOOP_VINFO_TARGET_COST_DATA (loop_vinfo), &vec_prologue_cost,
&vec_inside_cost, &vec_epilogue_cost);
vec_outside_cost = (int)(vec_prologue_cost + vec_epilogue_cost);
if (dump_enabled_p ())
{
dump_printf_loc (MSG_NOTE, vect_location, "Cost model analysis: \n");
dump_printf (MSG_NOTE, " Vector inside of loop cost: %d\n",
vec_inside_cost);
dump_printf (MSG_NOTE, " Vector prologue cost: %d\n",
vec_prologue_cost);
dump_printf (MSG_NOTE, " Vector epilogue cost: %d\n",
vec_epilogue_cost);
dump_printf (MSG_NOTE, " Scalar iteration cost: %d\n",
scalar_single_iter_cost);
dump_printf (MSG_NOTE, " Scalar outside cost: %d\n",
scalar_outside_cost);
dump_printf (MSG_NOTE, " Vector outside cost: %d\n",
vec_outside_cost);
dump_printf (MSG_NOTE, " prologue iterations: %d\n",
peel_iters_prologue);
dump_printf (MSG_NOTE, " epilogue iterations: %d\n",
peel_iters_epilogue);
}
/* Calculate number of iterations required to make the vector version
profitable, relative to the loop bodies only. The following condition
must hold true:
SIC * niters + SOC > VIC * ((niters-PL_ITERS-EP_ITERS)/VF) + VOC
where
SIC = scalar iteration cost, VIC = vector iteration cost,
VOC = vector outside cost, VF = vectorization factor,
PL_ITERS = prologue iterations, EP_ITERS= epilogue iterations
SOC = scalar outside cost for run time cost model check. */
if ((scalar_single_iter_cost * assumed_vf) > (int) vec_inside_cost)
{
min_profitable_iters = ((vec_outside_cost - scalar_outside_cost)
* assumed_vf
- vec_inside_cost * peel_iters_prologue
- vec_inside_cost * peel_iters_epilogue);
if (min_profitable_iters <= 0)
min_profitable_iters = 0;
else
{
min_profitable_iters /= ((scalar_single_iter_cost * assumed_vf)
- vec_inside_cost);
if ((scalar_single_iter_cost * assumed_vf * min_profitable_iters)
<= (((int) vec_inside_cost * min_profitable_iters)
+ (((int) vec_outside_cost - scalar_outside_cost)
* assumed_vf)))
min_profitable_iters++;
}
}
/* vector version will never be profitable. */
else
{
if (LOOP_VINFO_LOOP (loop_vinfo)->force_vectorize)
warning_at (vect_location, OPT_Wopenmp_simd, "vectorization "
"did not happen for a simd loop");
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"cost model: the vector iteration cost = %d "
"divided by the scalar iteration cost = %d "
"is greater or equal to the vectorization factor = %d"
".\n",
vec_inside_cost, scalar_single_iter_cost, assumed_vf);
*ret_min_profitable_niters = -1;
*ret_min_profitable_estimate = -1;
return;
}
dump_printf (MSG_NOTE,
" Calculated minimum iters for profitability: %d\n",
min_profitable_iters);
if (!LOOP_VINFO_FULLY_MASKED_P (loop_vinfo)
&& min_profitable_iters < (assumed_vf + peel_iters_prologue))
/* We want the vectorized loop to execute at least once. */
min_profitable_iters = assumed_vf + peel_iters_prologue;
if (dump_enabled_p ())
dump_printf_loc (MSG_NOTE, vect_location,
" Runtime profitability threshold = %d\n",
min_profitable_iters);
*ret_min_profitable_niters = min_profitable_iters;
/* Calculate number of iterations required to make the vector version
profitable, relative to the loop bodies only.
Non-vectorized variant is SIC * niters and it must win over vector
variant on the expected loop trip count. The following condition must hold true:
SIC * niters > VIC * ((niters-PL_ITERS-EP_ITERS)/VF) + VOC + SOC */
if (vec_outside_cost <= 0)
min_profitable_estimate = 0;
else
{
min_profitable_estimate = ((vec_outside_cost + scalar_outside_cost)
* assumed_vf
- vec_inside_cost * peel_iters_prologue
- vec_inside_cost * peel_iters_epilogue)
/ ((scalar_single_iter_cost * assumed_vf)
- vec_inside_cost);
}
min_profitable_estimate = MAX (min_profitable_estimate, min_profitable_iters);
if (dump_enabled_p ())
dump_printf_loc (MSG_NOTE, vect_location,
" Static estimate profitability threshold = %d\n",
min_profitable_estimate);
*ret_min_profitable_estimate = min_profitable_estimate;
}
/* Writes into SEL a mask for a vec_perm, equivalent to a vec_shr by OFFSET
vector elements (not bits) for a vector with NELT elements. */
static void
calc_vec_perm_mask_for_shift (unsigned int offset, unsigned int nelt,
vec_perm_builder *sel)
{
/* The encoding is a single stepped pattern. Any wrap-around is handled
by vec_perm_indices. */
sel->new_vector (nelt, 1, 3);
for (unsigned int i = 0; i < 3; i++)
sel->quick_push (i + offset);
}
/* Checks whether the target supports whole-vector shifts for vectors of mode
MODE. This is the case if _either_ the platform handles vec_shr_optab, _or_
it supports vec_perm_const with masks for all necessary shift amounts. */
static bool
have_whole_vector_shift (machine_mode mode)
{
if (optab_handler (vec_shr_optab, mode) != CODE_FOR_nothing)
return true;
/* Variable-length vectors should be handled via the optab. */
unsigned int nelt;
if (!GET_MODE_NUNITS (mode).is_constant (&nelt))
return false;
vec_perm_builder sel;
vec_perm_indices indices;
for (unsigned int i = nelt / 2; i >= 1; i /= 2)
{
calc_vec_perm_mask_for_shift (i, nelt, &sel);
indices.new_vector (sel, 2, nelt);
if (!can_vec_perm_const_p (mode, indices, false))
return false;
}
return true;
}
/* TODO: Close dependency between vect_model_*_cost and vectorizable_*
functions. Design better to avoid maintenance issues. */
/* Function vect_model_reduction_cost.
Models cost for a reduction operation, including the vector ops
generated within the strip-mine loop, the initial definition before
the loop, and the epilogue code that must be generated. */
static void
vect_model_reduction_cost (stmt_vec_info stmt_info, internal_fn reduc_fn,
int ncopies)
{
int prologue_cost = 0, epilogue_cost = 0, inside_cost;
enum tree_code code;
optab optab;
tree vectype;
gimple *orig_stmt;
machine_mode mode;
loop_vec_info loop_vinfo = STMT_VINFO_LOOP_VINFO (stmt_info);
struct loop *loop = NULL;
void *target_cost_data;
if (loop_vinfo)
{
loop = LOOP_VINFO_LOOP (loop_vinfo);
target_cost_data = LOOP_VINFO_TARGET_COST_DATA (loop_vinfo);
}
else
target_cost_data = BB_VINFO_TARGET_COST_DATA (STMT_VINFO_BB_VINFO (stmt_info));
/* Condition reductions generate two reductions in the loop. */
vect_reduction_type reduction_type
= STMT_VINFO_VEC_REDUCTION_TYPE (stmt_info);
if (reduction_type == COND_REDUCTION)
ncopies *= 2;
vectype = STMT_VINFO_VECTYPE (stmt_info);
mode = TYPE_MODE (vectype);
orig_stmt = STMT_VINFO_RELATED_STMT (stmt_info);
if (!orig_stmt)
orig_stmt = STMT_VINFO_STMT (stmt_info);
code = gimple_assign_rhs_code (orig_stmt);
if (reduction_type == EXTRACT_LAST_REDUCTION
|| reduction_type == FOLD_LEFT_REDUCTION)
{
/* No extra instructions needed in the prologue. */
prologue_cost = 0;
if (reduction_type == EXTRACT_LAST_REDUCTION || reduc_fn != IFN_LAST)
/* Count one reduction-like operation per vector. */
inside_cost = add_stmt_cost (target_cost_data, ncopies, vec_to_scalar,
stmt_info, 0, vect_body);
else
{
/* Use NELEMENTS extracts and NELEMENTS scalar ops. */
unsigned int nelements = ncopies * vect_nunits_for_cost (vectype);
inside_cost = add_stmt_cost (target_cost_data, nelements,
vec_to_scalar, stmt_info, 0,
vect_body);
inside_cost += add_stmt_cost (target_cost_data, nelements,
scalar_stmt, stmt_info, 0,
vect_body);
}
}
else
{
/* Add in cost for initial definition.
For cond reduction we have four vectors: initial index, step,
initial result of the data reduction, initial value of the index
reduction. */
int prologue_stmts = reduction_type == COND_REDUCTION ? 4 : 1;
prologue_cost += add_stmt_cost (target_cost_data, prologue_stmts,
scalar_to_vec, stmt_info, 0,
vect_prologue);
/* Cost of reduction op inside loop. */
inside_cost = add_stmt_cost (target_cost_data, ncopies, vector_stmt,
stmt_info, 0, vect_body);
}
/* Determine cost of epilogue code.
We have a reduction operator that will reduce the vector in one statement.
Also requires scalar extract. */
if (!loop || !nested_in_vect_loop_p (loop, orig_stmt))
{
if (reduc_fn != IFN_LAST)
{
if (reduction_type == COND_REDUCTION)
{
/* An EQ stmt and an COND_EXPR stmt. */
epilogue_cost += add_stmt_cost (target_cost_data, 2,
vector_stmt, stmt_info, 0,
vect_epilogue);
/* Reduction of the max index and a reduction of the found
values. */
epilogue_cost += add_stmt_cost (target_cost_data, 2,
vec_to_scalar, stmt_info, 0,
vect_epilogue);
/* A broadcast of the max value. */
epilogue_cost += add_stmt_cost (target_cost_data, 1,
scalar_to_vec, stmt_info, 0,
vect_epilogue);
}
else
{
epilogue_cost += add_stmt_cost (target_cost_data, 1, vector_stmt,
stmt_info, 0, vect_epilogue);
epilogue_cost += add_stmt_cost (target_cost_data, 1,
vec_to_scalar, stmt_info, 0,
vect_epilogue);
}
}
else if (reduction_type == COND_REDUCTION)
{
unsigned estimated_nunits = vect_nunits_for_cost (vectype);
/* Extraction of scalar elements. */
epilogue_cost += add_stmt_cost (target_cost_data,
2 * estimated_nunits,
vec_to_scalar, stmt_info, 0,
vect_epilogue);
/* Scalar max reductions via COND_EXPR / MAX_EXPR. */
epilogue_cost += add_stmt_cost (target_cost_data,
2 * estimated_nunits - 3,
scalar_stmt, stmt_info, 0,
vect_epilogue);
}
else if (reduction_type == EXTRACT_LAST_REDUCTION
|| reduction_type == FOLD_LEFT_REDUCTION)
/* No extra instructions need in the epilogue. */
;
else
{
int vec_size_in_bits = tree_to_uhwi (TYPE_SIZE (vectype));
tree bitsize =
TYPE_SIZE (TREE_TYPE (gimple_assign_lhs (orig_stmt)));
int element_bitsize = tree_to_uhwi (bitsize);
int nelements = vec_size_in_bits / element_bitsize;
if (code == COND_EXPR)
code = MAX_EXPR;
optab = optab_for_tree_code (code, vectype, optab_default);
/* We have a whole vector shift available. */
if (optab != unknown_optab
&& VECTOR_MODE_P (mode)
&& optab_handler (optab, mode) != CODE_FOR_nothing
&& have_whole_vector_shift (mode))
{
/* Final reduction via vector shifts and the reduction operator.
Also requires scalar extract. */
epilogue_cost += add_stmt_cost (target_cost_data,
exact_log2 (nelements) * 2,
vector_stmt, stmt_info, 0,
vect_epilogue);
epilogue_cost += add_stmt_cost (target_cost_data, 1,
vec_to_scalar, stmt_info, 0,
vect_epilogue);
}
else
/* Use extracts and reduction op for final reduction. For N
elements, we have N extracts and N-1 reduction ops. */
epilogue_cost += add_stmt_cost (target_cost_data,
nelements + nelements - 1,
vector_stmt, stmt_info, 0,
vect_epilogue);
}
}
if (dump_enabled_p ())
dump_printf (MSG_NOTE,
"vect_model_reduction_cost: inside_cost = %d, "
"prologue_cost = %d, epilogue_cost = %d .\n", inside_cost,
prologue_cost, epilogue_cost);
}
/* Function vect_model_induction_cost.
Models cost for induction operations. */
static void
vect_model_induction_cost (stmt_vec_info stmt_info, int ncopies)
{
loop_vec_info loop_vinfo = STMT_VINFO_LOOP_VINFO (stmt_info);
void *target_cost_data = LOOP_VINFO_TARGET_COST_DATA (loop_vinfo);
unsigned inside_cost, prologue_cost;
if (PURE_SLP_STMT (stmt_info))
return;
/* loop cost for vec_loop. */
inside_cost = add_stmt_cost (target_cost_data, ncopies, vector_stmt,
stmt_info, 0, vect_body);
/* prologue cost for vec_init and vec_step. */
prologue_cost = add_stmt_cost (target_cost_data, 2, scalar_to_vec,
stmt_info, 0, vect_prologue);
if (dump_enabled_p ())
dump_printf_loc (MSG_NOTE, vect_location,
"vect_model_induction_cost: inside_cost = %d, "
"prologue_cost = %d .\n", inside_cost, prologue_cost);
}
/* Function get_initial_def_for_reduction
Input:
STMT - a stmt that performs a reduction operation in the loop.
INIT_VAL - the initial value of the reduction variable
Output:
ADJUSTMENT_DEF - a tree that holds a value to be added to the final result
of the reduction (used for adjusting the epilog - see below).
Return a vector variable, initialized according to the operation that STMT
performs. This vector will be used as the initial value of the
vector of partial results.
Option1 (adjust in epilog): Initialize the vector as follows:
add/bit or/xor: [0,0,...,0,0]
mult/bit and: [1,1,...,1,1]
min/max/cond_expr: [init_val,init_val,..,init_val,init_val]
and when necessary (e.g. add/mult case) let the caller know
that it needs to adjust the result by init_val.
Option2: Initialize the vector as follows:
add/bit or/xor: [init_val,0,0,...,0]
mult/bit and: [init_val,1,1,...,1]
min/max/cond_expr: [init_val,init_val,...,init_val]
and no adjustments are needed.
For example, for the following code:
s = init_val;
for (i=0;i<n;i++)
s = s + a[i];
STMT is 's = s + a[i]', and the reduction variable is 's'.
For a vector of 4 units, we want to return either [0,0,0,init_val],
or [0,0,0,0] and let the caller know that it needs to adjust
the result at the end by 'init_val'.
FORNOW, we are using the 'adjust in epilog' scheme, because this way the
initialization vector is simpler (same element in all entries), if
ADJUSTMENT_DEF is not NULL, and Option2 otherwise.
A cost model should help decide between these two schemes. */
tree
get_initial_def_for_reduction (gimple *stmt, tree init_val,
tree *adjustment_def)
{
stmt_vec_info stmt_vinfo = vinfo_for_stmt (stmt);
loop_vec_info loop_vinfo = STMT_VINFO_LOOP_VINFO (stmt_vinfo);
struct loop *loop = LOOP_VINFO_LOOP (loop_vinfo);
tree scalar_type = TREE_TYPE (init_val);
tree vectype = get_vectype_for_scalar_type (scalar_type);
enum tree_code code = gimple_assign_rhs_code (stmt);
tree def_for_init;
tree init_def;
bool nested_in_vect_loop = false;
REAL_VALUE_TYPE real_init_val = dconst0;
int int_init_val = 0;
gimple *def_stmt = NULL;
gimple_seq stmts = NULL;
gcc_assert (vectype);
gcc_assert (POINTER_TYPE_P (scalar_type) || INTEGRAL_TYPE_P (scalar_type)
|| SCALAR_FLOAT_TYPE_P (scalar_type));
if (nested_in_vect_loop_p (loop, stmt))
nested_in_vect_loop = true;
else
gcc_assert (loop == (gimple_bb (stmt))->loop_father);
/* In case of double reduction we only create a vector variable to be put
in the reduction phi node. The actual statement creation is done in
vect_create_epilog_for_reduction. */
if (adjustment_def && nested_in_vect_loop
&& TREE_CODE (init_val) == SSA_NAME
&& (def_stmt = SSA_NAME_DEF_STMT (init_val))
&& gimple_code (def_stmt) == GIMPLE_PHI
&& flow_bb_inside_loop_p (loop, gimple_bb (def_stmt))
&& vinfo_for_stmt (def_stmt)
&& STMT_VINFO_DEF_TYPE (vinfo_for_stmt (def_stmt))
== vect_double_reduction_def)
{
*adjustment_def = NULL;
return vect_create_destination_var (init_val, vectype);
}
vect_reduction_type reduction_type
= STMT_VINFO_VEC_REDUCTION_TYPE (stmt_vinfo);
/* In case of a nested reduction do not use an adjustment def as
that case is not supported by the epilogue generation correctly
if ncopies is not one. */
if (adjustment_def && nested_in_vect_loop)
{
*adjustment_def = NULL;
return vect_get_vec_def_for_operand (init_val, stmt);
}
switch (code)
{
case WIDEN_SUM_EXPR:
case DOT_PROD_EXPR:
case SAD_EXPR:
case PLUS_EXPR:
case MINUS_EXPR:
case BIT_IOR_EXPR:
case BIT_XOR_EXPR:
case MULT_EXPR:
case BIT_AND_EXPR:
{
/* ADJUSTMENT_DEF is NULL when called from
vect_create_epilog_for_reduction to vectorize double reduction. */
if (adjustment_def)
*adjustment_def = init_val;
if (code == MULT_EXPR)
{
real_init_val = dconst1;
int_init_val = 1;
}
if (code == BIT_AND_EXPR)
int_init_val = -1;
if (SCALAR_FLOAT_TYPE_P (scalar_type))
def_for_init = build_real (scalar_type, real_init_val);
else
def_for_init = build_int_cst (scalar_type, int_init_val);
if (adjustment_def)
/* Option1: the first element is '0' or '1' as well. */
init_def = gimple_build_vector_from_val (&stmts, vectype,
def_for_init);
else if (!TYPE_VECTOR_SUBPARTS (vectype).is_constant ())
{
/* Option2 (variable length): the first element is INIT_VAL. */
init_def = build_vector_from_val (vectype, def_for_init);
gcall *call = gimple_build_call_internal (IFN_VEC_SHL_INSERT,
2, init_def, init_val);
init_def = make_ssa_name (vectype);
gimple_call_set_lhs (call, init_def);
gimple_seq_add_stmt (&stmts, call);
}
else
{
/* Option2: the first element is INIT_VAL. */
tree_vector_builder elts (vectype, 1, 2);
elts.quick_push (init_val);
elts.quick_push (def_for_init);
init_def = gimple_build_vector (&stmts, &elts);
}
}
break;
case MIN_EXPR:
case MAX_EXPR:
case COND_EXPR:
{
if (adjustment_def)
{
*adjustment_def = NULL_TREE;
if (reduction_type != COND_REDUCTION
&& reduction_type != EXTRACT_LAST_REDUCTION)
{
init_def = vect_get_vec_def_for_operand (init_val, stmt);
break;
}
}
init_val = gimple_convert (&stmts, TREE_TYPE (vectype), init_val);
init_def = gimple_build_vector_from_val (&stmts, vectype, init_val);
}
break;
default:
gcc_unreachable ();
}
if (stmts)
gsi_insert_seq_on_edge_immediate (loop_preheader_edge (loop), stmts);
return init_def;
}
/* Get at the initial defs for the reduction PHIs in SLP_NODE.
NUMBER_OF_VECTORS is the number of vector defs to create.
If NEUTRAL_OP is nonnull, introducing extra elements of that
value will not change the result. */
static void
get_initial_defs_for_reduction (slp_tree slp_node,
vec<tree> *vec_oprnds,
unsigned int number_of_vectors,
bool reduc_chain, tree neutral_op)
{
vec<gimple *> stmts = SLP_TREE_SCALAR_STMTS (slp_node);
gimple *stmt = stmts[0];
stmt_vec_info stmt_vinfo = vinfo_for_stmt (stmt);
unsigned HOST_WIDE_INT nunits;
unsigned j, number_of_places_left_in_vector;
tree vector_type;
tree vop;
int group_size = stmts.length ();
unsigned int vec_num, i;
unsigned number_of_copies = 1;
vec<tree> voprnds;
voprnds.create (number_of_vectors);
struct loop *loop;
auto_vec<tree, 16> permute_results;
vector_type = STMT_VINFO_VECTYPE (stmt_vinfo);
gcc_assert (STMT_VINFO_DEF_TYPE (stmt_vinfo) == vect_reduction_def);
loop = (gimple_bb (stmt))->loop_father;
gcc_assert (loop);
edge pe = loop_preheader_edge (loop);
gcc_assert (!reduc_chain || neutral_op);
/* NUMBER_OF_COPIES is the number of times we need to use the same values in
created vectors. It is greater than 1 if unrolling is performed.
For example, we have two scalar operands, s1 and s2 (e.g., group of
strided accesses of size two), while NUNITS is four (i.e., four scalars
of this type can be packed in a vector). The output vector will contain
two copies of each scalar operand: {s1, s2, s1, s2}. (NUMBER_OF_COPIES
will be 2).
If GROUP_SIZE > NUNITS, the scalars will be split into several vectors
containing the operands.
For example, NUNITS is four as before, and the group size is 8
(s1, s2, ..., s8). We will create two vectors {s1, s2, s3, s4} and
{s5, s6, s7, s8}. */
if (!TYPE_VECTOR_SUBPARTS (vector_type).is_constant (&nunits))
nunits = group_size;
number_of_copies = nunits * number_of_vectors / group_size;
number_of_places_left_in_vector = nunits;
bool constant_p = true;
tree_vector_builder elts (vector_type, nunits, 1);
elts.quick_grow (nunits);
for (j = 0; j < number_of_copies; j++)
{
for (i = group_size - 1; stmts.iterate (i, &stmt); i--)
{
tree op;
/* Get the def before the loop. In reduction chain we have only
one initial value. */
if ((j != (number_of_copies - 1)
|| (reduc_chain && i != 0))
&& neutral_op)
op = neutral_op;
else
op = PHI_ARG_DEF_FROM_EDGE (stmt, pe);
/* Create 'vect_ = {op0,op1,...,opn}'. */
number_of_places_left_in_vector--;
elts[number_of_places_left_in_vector] = op;
if (!CONSTANT_CLASS_P (op))
constant_p = false;
if (number_of_places_left_in_vector == 0)
{
gimple_seq ctor_seq = NULL;
tree init;
if (constant_p && !neutral_op
? multiple_p (TYPE_VECTOR_SUBPARTS (vector_type), nunits)
: known_eq (TYPE_VECTOR_SUBPARTS (vector_type), nunits))
/* Build the vector directly from ELTS. */
init = gimple_build_vector (&ctor_seq, &elts);
else if (neutral_op)
{
/* Build a vector of the neutral value and shift the
other elements into place. */
init = gimple_build_vector_from_val (&ctor_seq, vector_type,
neutral_op);
int k = nunits;
while (k > 0 && elts[k - 1] == neutral_op)
k -= 1;
while (k > 0)
{
k -= 1;
gcall *call = gimple_build_call_internal
(IFN_VEC_SHL_INSERT, 2, init, elts[k]);
init = make_ssa_name (vector_type);
gimple_call_set_lhs (call, init);
gimple_seq_add_stmt (&ctor_seq, call);
}
}
else
{
/* First time round, duplicate ELTS to fill the
required number of vectors, then cherry pick the
appropriate result for each iteration. */
if (vec_oprnds->is_empty ())
duplicate_and_interleave (&ctor_seq, vector_type, elts,
number_of_vectors,
permute_results);
init = permute_results[number_of_vectors - j - 1];
}
if (ctor_seq != NULL)
gsi_insert_seq_on_edge_immediate (pe, ctor_seq);
voprnds.quick_push (init);
number_of_places_left_in_vector = nunits;
elts.new_vector (vector_type, nunits, 1);
elts.quick_grow (nunits);
constant_p = true;
}
}
}
/* Since the vectors are created in the reverse order, we should invert
them. */
vec_num = voprnds.length ();
for (j = vec_num; j != 0; j--)
{
vop = voprnds[j - 1];
vec_oprnds->quick_push (vop);
}
voprnds.release ();
/* In case that VF is greater than the unrolling factor needed for the SLP
group of stmts, NUMBER_OF_VECTORS to be created is greater than
NUMBER_OF_SCALARS/NUNITS or NUNITS/NUMBER_OF_SCALARS, and hence we have
to replicate the vectors. */
tree neutral_vec = NULL;
while (number_of_vectors > vec_oprnds->length ())
{
if (neutral_op)
{
if (!neutral_vec)
{
gimple_seq ctor_seq = NULL;
neutral_vec = gimple_build_vector_from_val
(&ctor_seq, vector_type, neutral_op);
if (ctor_seq != NULL)
gsi_insert_seq_on_edge_immediate (pe, ctor_seq);
}
vec_oprnds->quick_push (neutral_vec);
}
else
{
for (i = 0; vec_oprnds->iterate (i, &vop) && i < vec_num; i++)
vec_oprnds->quick_push (vop);
}
}
}
/* Function vect_create_epilog_for_reduction
Create code at the loop-epilog to finalize the result of a reduction
computation.
VECT_DEFS is list of vector of partial results, i.e., the lhs's of vector
reduction statements.
STMT is the scalar reduction stmt that is being vectorized.
NCOPIES is > 1 in case the vectorization factor (VF) is bigger than the
number of elements that we can fit in a vectype (nunits). In this case
we have to generate more than one vector stmt - i.e - we need to "unroll"
the vector stmt by a factor VF/nunits. For more details see documentation
in vectorizable_operation.
REDUC_FN is the internal function for the epilog reduction.
REDUCTION_PHIS is a list of the phi-nodes that carry the reduction
computation.
REDUC_INDEX is the index of the operand in the right hand side of the
statement that is defined by REDUCTION_PHI.
DOUBLE_REDUC is TRUE if double reduction phi nodes should be handled.
SLP_NODE is an SLP node containing a group of reduction statements. The
first one in this group is STMT.
INDUC_VAL is for INTEGER_INDUC_COND_REDUCTION the value to use for the case
when the COND_EXPR is never true in the loop. For MAX_EXPR, it needs to
be smaller than any value of the IV in the loop, for MIN_EXPR larger than
any value of the IV in the loop.
INDUC_CODE is the code for epilog reduction if INTEGER_INDUC_COND_REDUCTION.
NEUTRAL_OP is the value given by neutral_op_for_slp_reduction; it is
null if this is not an SLP reduction
This function:
1. Creates the reduction def-use cycles: sets the arguments for
REDUCTION_PHIS:
The loop-entry argument is the vectorized initial-value of the reduction.
The loop-latch argument is taken from VECT_DEFS - the vector of partial
sums.
2. "Reduces" each vector of partial results VECT_DEFS into a single result,
by calling the function specified by REDUC_FN if available, or by
other means (whole-vector shifts or a scalar loop).
The function also creates a new phi node at the loop exit to preserve
loop-closed form, as illustrated below.
The flow at the entry to this function:
loop:
vec_def = phi <null, null> # REDUCTION_PHI
VECT_DEF = vector_stmt # vectorized form of STMT
s_loop = scalar_stmt # (scalar) STMT
loop_exit:
s_out0 = phi <s_loop> # (scalar) EXIT_PHI
use <s_out0>
use <s_out0>
The above is transformed by this function into:
loop:
vec_def = phi <vec_init, VECT_DEF> # REDUCTION_PHI
VECT_DEF = vector_stmt # vectorized form of STMT
s_loop = scalar_stmt # (scalar) STMT
loop_exit:
s_out0 = phi <s_loop> # (scalar) EXIT_PHI
v_out1 = phi <VECT_DEF> # NEW_EXIT_PHI
v_out2 = reduce <v_out1>
s_out3 = extract_field <v_out2, 0>
s_out4 = adjust_result <s_out3>
use <s_out4>
use <s_out4>
*/
static void
vect_create_epilog_for_reduction (vec<tree> vect_defs, gimple *stmt,
gimple *reduc_def_stmt,
int ncopies, internal_fn reduc_fn,
vec<gimple *> reduction_phis,
bool double_reduc,
slp_tree slp_node,
slp_instance slp_node_instance,
tree induc_val, enum tree_code induc_code,
tree neutral_op)
{
stmt_vec_info stmt_info = vinfo_for_stmt (stmt);
stmt_vec_info prev_phi_info;
tree vectype;
machine_mode mode;
loop_vec_info loop_vinfo = STMT_VINFO_LOOP_VINFO (stmt_info);
struct loop *loop = LOOP_VINFO_LOOP (loop_vinfo), *outer_loop = NULL;
basic_block exit_bb;
tree scalar_dest;
tree scalar_type;
gimple *new_phi = NULL, *phi;
gimple_stmt_iterator exit_gsi;
tree vec_dest;
tree new_temp = NULL_TREE, new_dest, new_name, new_scalar_dest;
gimple *epilog_stmt = NULL;
enum tree_code code = gimple_assign_rhs_code (stmt);
gimple *exit_phi;
tree bitsize;
tree adjustment_def = NULL;
tree vec_initial_def = NULL;
tree expr, def, initial_def = NULL;
tree orig_name, scalar_result;
imm_use_iterator imm_iter, phi_imm_iter;
use_operand_p use_p, phi_use_p;
gimple *use_stmt, *orig_stmt, *reduction_phi = NULL;
bool nested_in_vect_loop = false;
auto_vec<gimple *> new_phis;
auto_vec<gimple *> inner_phis;
enum vect_def_type dt = vect_unknown_def_type;
int j, i;
auto_vec<tree> scalar_results;
unsigned int group_size = 1, k, ratio;
auto_vec<tree> vec_initial_defs;
auto_vec<gimple *> phis;
bool slp_reduc = false;
bool direct_slp_reduc;
tree new_phi_result;
gimple *inner_phi = NULL;
tree induction_index = NULL_TREE;
if (slp_node)
group_size = SLP_TREE_SCALAR_STMTS (slp_node).length ();
if (nested_in_vect_loop_p (loop, stmt))
{
outer_loop = loop;
loop = loop->inner;
nested_in_vect_loop = true;
gcc_assert (!slp_node);
}
vectype = STMT_VINFO_VECTYPE (stmt_info);
gcc_assert (vectype);
mode = TYPE_MODE (vectype);
/* 1. Create the reduction def-use cycle:
Set the arguments of REDUCTION_PHIS, i.e., transform
loop:
vec_def = phi <null, null> # REDUCTION_PHI
VECT_DEF = vector_stmt # vectorized form of STMT
...
into:
loop:
vec_def = phi <vec_init, VECT_DEF> # REDUCTION_PHI
VECT_DEF = vector_stmt # vectorized form of STMT
...
(in case of SLP, do it for all the phis). */
/* Get the loop-entry arguments. */
enum vect_def_type initial_def_dt = vect_unknown_def_type;
if (slp_node)
{
unsigned vec_num = SLP_TREE_NUMBER_OF_VEC_STMTS (slp_node);
vec_initial_defs.reserve (vec_num);
get_initial_defs_for_reduction (slp_node_instance->reduc_phis,
&vec_initial_defs, vec_num,
GROUP_FIRST_ELEMENT (stmt_info),
neutral_op);
}
else
{
/* Get at the scalar def before the loop, that defines the initial value
of the reduction variable. */
gimple *def_stmt;
initial_def = PHI_ARG_DEF_FROM_EDGE (reduc_def_stmt,
loop_preheader_edge (loop));
/* Optimize: if initial_def is for REDUC_MAX smaller than the base
and we can't use zero for induc_val, use initial_def. Similarly
for REDUC_MIN and initial_def larger than the base. */
if (TREE_CODE (initial_def) == INTEGER_CST
&& (STMT_VINFO_VEC_REDUCTION_TYPE (stmt_info)
== INTEGER_INDUC_COND_REDUCTION)
&& !integer_zerop (induc_val)
&& ((induc_code == MAX_EXPR
&& tree_int_cst_lt (initial_def, induc_val))
|| (induc_code == MIN_EXPR
&& tree_int_cst_lt (induc_val, initial_def))))
induc_val = initial_def;
vect_is_simple_use (initial_def, loop_vinfo, &def_stmt, &initial_def_dt);
vec_initial_def = get_initial_def_for_reduction (stmt, initial_def,
&adjustment_def);
vec_initial_defs.create (1);
vec_initial_defs.quick_push (vec_initial_def);
}
/* Set phi nodes arguments. */
FOR_EACH_VEC_ELT (reduction_phis, i, phi)
{
tree vec_init_def = vec_initial_defs[i];
tree def = vect_defs[i];
for (j = 0; j < ncopies; j++)
{
if (j != 0)
{
phi = STMT_VINFO_RELATED_STMT (vinfo_for_stmt (phi));
if (nested_in_vect_loop)
vec_init_def
= vect_get_vec_def_for_stmt_copy (initial_def_dt,
vec_init_def);
}
/* Set the loop-entry arg of the reduction-phi. */
if (STMT_VINFO_VEC_REDUCTION_TYPE (stmt_info)
== INTEGER_INDUC_COND_REDUCTION)
{
/* Initialise the reduction phi to zero. This prevents initial
values of non-zero interferring with the reduction op. */
gcc_assert (ncopies == 1);
gcc_assert (i == 0);
tree vec_init_def_type = TREE_TYPE (vec_init_def);
tree induc_val_vec
= build_vector_from_val (vec_init_def_type, induc_val);
add_phi_arg (as_a <gphi *> (phi), induc_val_vec,
loop_preheader_edge (loop), UNKNOWN_LOCATION);
}
else
add_phi_arg (as_a <gphi *> (phi), vec_init_def,
loop_preheader_edge (loop), UNKNOWN_LOCATION);
/* Set the loop-latch arg for the reduction-phi. */
if (j > 0)
def = vect_get_vec_def_for_stmt_copy (vect_unknown_def_type, def);
add_phi_arg (as_a <gphi *> (phi), def, loop_latch_edge (loop),
UNKNOWN_LOCATION);
if (dump_enabled_p ())
{
dump_printf_loc (MSG_NOTE, vect_location,
"transform reduction: created def-use cycle: ");
dump_gimple_stmt (MSG_NOTE, TDF_SLIM, phi, 0);
dump_gimple_stmt (MSG_NOTE, TDF_SLIM, SSA_NAME_DEF_STMT (def), 0);
}
}
}
/* For cond reductions we want to create a new vector (INDEX_COND_EXPR)
which is updated with the current index of the loop for every match of
the original loop's cond_expr (VEC_STMT). This results in a vector
containing the last time the condition passed for that vector lane.
The first match will be a 1 to allow 0 to be used for non-matching
indexes. If there are no matches at all then the vector will be all
zeroes. */
if (STMT_VINFO_VEC_REDUCTION_TYPE (stmt_info) == COND_REDUCTION)
{
tree indx_before_incr, indx_after_incr;
poly_uint64 nunits_out = TYPE_VECTOR_SUBPARTS (vectype);
gimple *vec_stmt = STMT_VINFO_VEC_STMT (stmt_info);
gcc_assert (gimple_assign_rhs_code (vec_stmt) == VEC_COND_EXPR);
int scalar_precision
= GET_MODE_PRECISION (SCALAR_TYPE_MODE (TREE_TYPE (vectype)));
tree cr_index_scalar_type = make_unsigned_type (scalar_precision);
tree cr_index_vector_type = build_vector_type
(cr_index_scalar_type, TYPE_VECTOR_SUBPARTS (vectype));
/* First we create a simple vector induction variable which starts
with the values {1,2,3,...} (SERIES_VECT) and increments by the
vector size (STEP). */
/* Create a {1,2,3,...} vector. */
tree series_vect = build_index_vector (cr_index_vector_type, 1, 1);
/* Create a vector of the step value. */
tree step = build_int_cst (cr_index_scalar_type, nunits_out);
tree vec_step = build_vector_from_val (cr_index_vector_type, step);
/* Create an induction variable. */
gimple_stmt_iterator incr_gsi;
bool insert_after;
standard_iv_increment_position (loop, &incr_gsi, &insert_after);
create_iv (series_vect, vec_step, NULL_TREE, loop, &incr_gsi,
insert_after, &indx_before_incr, &indx_after_incr);
/* Next create a new phi node vector (NEW_PHI_TREE) which starts
filled with zeros (VEC_ZERO). */
/* Create a vector of 0s. */
tree zero = build_zero_cst (cr_index_scalar_type);
tree vec_zero = build_vector_from_val (cr_index_vector_type, zero);
/* Create a vector phi node. */
tree new_phi_tree = make_ssa_name (cr_index_vector_type);
new_phi = create_phi_node (new_phi_tree, loop->header);
set_vinfo_for_stmt (new_phi,
new_stmt_vec_info (new_phi, loop_vinfo));
add_phi_arg (as_a <gphi *> (new_phi), vec_zero,
loop_preheader_edge (loop), UNKNOWN_LOCATION);
/* Now take the condition from the loops original cond_expr
(VEC_STMT) and produce a new cond_expr (INDEX_COND_EXPR) which for
every match uses values from the induction variable
(INDEX_BEFORE_INCR) otherwise uses values from the phi node
(NEW_PHI_TREE).
Finally, we update the phi (NEW_PHI_TREE) to take the value of
the new cond_expr (INDEX_COND_EXPR). */
/* Duplicate the condition from vec_stmt. */
tree ccompare = unshare_expr (gimple_assign_rhs1 (vec_stmt));
/* Create a conditional, where the condition is taken from vec_stmt
(CCOMPARE), then is the induction index (INDEX_BEFORE_INCR) and
else is the phi (NEW_PHI_TREE). */
tree index_cond_expr = build3 (VEC_COND_EXPR, cr_index_vector_type,
ccompare, indx_before_incr,
new_phi_tree);
induction_index = make_ssa_name (cr_index_vector_type);
gimple *index_condition = gimple_build_assign (induction_index,
index_cond_expr);
gsi_insert_before (&incr_gsi, index_condition, GSI_SAME_STMT);
stmt_vec_info index_vec_info = new_stmt_vec_info (index_condition,
loop_vinfo);
STMT_VINFO_VECTYPE (index_vec_info) = cr_index_vector_type;
set_vinfo_for_stmt (index_condition, index_vec_info);
/* Update the phi with the vec cond. */
add_phi_arg (as_a <gphi *> (new_phi), induction_index,
loop_latch_edge (loop), UNKNOWN_LOCATION);
}
/* 2. Create epilog code.
The reduction epilog code operates across the elements of the vector
of partial results computed by the vectorized loop.
The reduction epilog code consists of:
step 1: compute the scalar result in a vector (v_out2)
step 2: extract the scalar result (s_out3) from the vector (v_out2)
step 3: adjust the scalar result (s_out3) if needed.
Step 1 can be accomplished using one the following three schemes:
(scheme 1) using reduc_fn, if available.
(scheme 2) using whole-vector shifts, if available.
(scheme 3) using a scalar loop. In this case steps 1+2 above are
combined.
The overall epilog code looks like this:
s_out0 = phi <s_loop> # original EXIT_PHI
v_out1 = phi <VECT_DEF> # NEW_EXIT_PHI
v_out2 = reduce <v_out1> # step 1
s_out3 = extract_field <v_out2, 0> # step 2
s_out4 = adjust_result <s_out3> # step 3
(step 3 is optional, and steps 1 and 2 may be combined).
Lastly, the uses of s_out0 are replaced by s_out4. */
/* 2.1 Create new loop-exit-phis to preserve loop-closed form:
v_out1 = phi <VECT_DEF>
Store them in NEW_PHIS. */
exit_bb = single_exit (loop)->dest;
prev_phi_info = NULL;
new_phis.create (vect_defs.length ());
FOR_EACH_VEC_ELT (vect_defs, i, def)
{
for (j = 0; j < ncopies; j++)
{
tree new_def = copy_ssa_name (def);
phi = create_phi_node (new_def, exit_bb);
set_vinfo_for_stmt (phi, new_stmt_vec_info (phi, loop_vinfo));
if (j == 0)
new_phis.quick_push (phi);
else
{
def = vect_get_vec_def_for_stmt_copy (dt, def);
STMT_VINFO_RELATED_STMT (prev_phi_info) = phi;
}
SET_PHI_ARG_DEF (phi, single_exit (loop)->dest_idx, def);
prev_phi_info = vinfo_for_stmt (phi);
}
}
/* The epilogue is created for the outer-loop, i.e., for the loop being
vectorized. Create exit phis for the outer loop. */
if (double_reduc)
{
loop = outer_loop;
exit_bb = single_exit (loop)->dest;
inner_phis.create (vect_defs.length ());
FOR_EACH_VEC_ELT (new_phis, i, phi)
{
tree new_result = copy_ssa_name (PHI_RESULT (phi));
gphi *outer_phi = create_phi_node (new_result, exit_bb);
SET_PHI_ARG_DEF (outer_phi, single_exit (loop)->dest_idx,
PHI_RESULT (phi));
set_vinfo_for_stmt (outer_phi, new_stmt_vec_info (outer_phi,
loop_vinfo));
inner_phis.quick_push (phi);
new_phis[i] = outer_phi;
prev_phi_info = vinfo_for_stmt (outer_phi);
while (STMT_VINFO_RELATED_STMT (vinfo_for_stmt (phi)))
{
phi = STMT_VINFO_RELATED_STMT (vinfo_for_stmt (phi));
new_result = copy_ssa_name (PHI_RESULT (phi));
outer_phi = create_phi_node (new_result, exit_bb);
SET_PHI_ARG_DEF (outer_phi, single_exit (loop)->dest_idx,
PHI_RESULT (phi));
set_vinfo_for_stmt (outer_phi, new_stmt_vec_info (outer_phi,
loop_vinfo));
STMT_VINFO_RELATED_STMT (prev_phi_info) = outer_phi;
prev_phi_info = vinfo_for_stmt (outer_phi);
}
}
}
exit_gsi = gsi_after_labels (exit_bb);
/* 2.2 Get the relevant tree-code to use in the epilog for schemes 2,3
(i.e. when reduc_fn is not available) and in the final adjustment
code (if needed). Also get the original scalar reduction variable as
defined in the loop. In case STMT is a "pattern-stmt" (i.e. - it
represents a reduction pattern), the tree-code and scalar-def are
taken from the original stmt that the pattern-stmt (STMT) replaces.
Otherwise (it is a regular reduction) - the tree-code and scalar-def
are taken from STMT. */
orig_stmt = STMT_VINFO_RELATED_STMT (stmt_info);
if (!orig_stmt)
{
/* Regular reduction */
orig_stmt = stmt;
}
else
{
/* Reduction pattern */
stmt_vec_info stmt_vinfo = vinfo_for_stmt (orig_stmt);
gcc_assert (STMT_VINFO_IN_PATTERN_P (stmt_vinfo));
gcc_assert (STMT_VINFO_RELATED_STMT (stmt_vinfo) == stmt);
}
code = gimple_assign_rhs_code (orig_stmt);
/* For MINUS_EXPR the initial vector is [init_val,0,...,0], therefore,
partial results are added and not subtracted. */
if (code == MINUS_EXPR)
code = PLUS_EXPR;
scalar_dest = gimple_assign_lhs (orig_stmt);
scalar_type = TREE_TYPE (scalar_dest);
scalar_results.create (group_size);
new_scalar_dest = vect_create_destination_var (scalar_dest, NULL);
bitsize = TYPE_SIZE (scalar_type);
/* In case this is a reduction in an inner-loop while vectorizing an outer
loop - we don't need to extract a single scalar result at the end of the
inner-loop (unless it is double reduction, i.e., the use of reduction is
outside the outer-loop). The final vector of partial results will be used
in the vectorized outer-loop, or reduced to a scalar result at the end of
the outer-loop. */
if (nested_in_vect_loop && !double_reduc)
goto vect_finalize_reduction;
/* SLP reduction without reduction chain, e.g.,
# a1 = phi <a2, a0>
# b1 = phi <b2, b0>
a2 = operation (a1)
b2 = operation (b1) */
slp_reduc = (slp_node && !GROUP_FIRST_ELEMENT (vinfo_for_stmt (stmt)));
/* True if we should implement SLP_REDUC using native reduction operations
instead of scalar operations. */
direct_slp_reduc = (reduc_fn != IFN_LAST
&& slp_reduc
&& !TYPE_VECTOR_SUBPARTS (vectype).is_constant ());
/* In case of reduction chain, e.g.,
# a1 = phi <a3, a0>
a2 = operation (a1)
a3 = operation (a2),
we may end up with more than one vector result. Here we reduce them to
one vector. */
if (GROUP_FIRST_ELEMENT (vinfo_for_stmt (stmt)) || direct_slp_reduc)
{
tree first_vect = PHI_RESULT (new_phis[0]);
gassign *new_vec_stmt = NULL;
vec_dest = vect_create_destination_var (scalar_dest, vectype);
for (k = 1; k < new_phis.length (); k++)
{
gimple *next_phi = new_phis[k];
tree second_vect = PHI_RESULT (next_phi);
tree tem = make_ssa_name (vec_dest, new_vec_stmt);
new_vec_stmt = gimple_build_assign (tem, code,
first_vect, second_vect);
gsi_insert_before (&exit_gsi, new_vec_stmt, GSI_SAME_STMT);
first_vect = tem;
}
new_phi_result = first_vect;
if (new_vec_stmt)
{
new_phis.truncate (0);
new_phis.safe_push (new_vec_stmt);
}
}
/* Likewise if we couldn't use a single defuse cycle. */
else if (ncopies > 1)
{
gcc_assert (new_phis.length () == 1);
tree first_vect = PHI_RESULT (new_phis[0]);
gassign *new_vec_stmt = NULL;
vec_dest = vect_create_destination_var (scalar_dest, vectype);
gimple *next_phi = new_phis[0];
for (int k = 1; k < ncopies; ++k)
{
next_phi = STMT_VINFO_RELATED_STMT (vinfo_for_stmt (next_phi));
tree second_vect = PHI_RESULT (next_phi);
tree tem = make_ssa_name (vec_dest, new_vec_stmt);
new_vec_stmt = gimple_build_assign (tem, code,
first_vect, second_vect);
gsi_insert_before (&exit_gsi, new_vec_stmt, GSI_SAME_STMT);
first_vect = tem;
}
new_phi_result = first_vect;
new_phis.truncate (0);
new_phis.safe_push (new_vec_stmt);
}
else
new_phi_result = PHI_RESULT (new_phis[0]);
if (STMT_VINFO_VEC_REDUCTION_TYPE (stmt_info) == COND_REDUCTION
&& reduc_fn != IFN_LAST)
{
/* For condition reductions, we have a vector (NEW_PHI_RESULT) containing
various data values where the condition matched and another vector
(INDUCTION_INDEX) containing all the indexes of those matches. We
need to extract the last matching index (which will be the index with
highest value) and use this to index into the data vector.
For the case where there were no matches, the data vector will contain
all default values and the index vector will be all zeros. */
/* Get various versions of the type of the vector of indexes. */
tree index_vec_type = TREE_TYPE (induction_index);
gcc_checking_assert (TYPE_UNSIGNED (index_vec_type));
tree index_scalar_type = TREE_TYPE (index_vec_type);
tree index_vec_cmp_type = build_same_sized_truth_vector_type
(index_vec_type);
/* Get an unsigned integer version of the type of the data vector. */
int scalar_precision
= GET_MODE_PRECISION (SCALAR_TYPE_MODE (scalar_type));
tree scalar_type_unsigned = make_unsigned_type (scalar_precision);
tree vectype_unsigned = build_vector_type
(scalar_type_unsigned, TYPE_VECTOR_SUBPARTS (vectype));
/* First we need to create a vector (ZERO_VEC) of zeros and another
vector (MAX_INDEX_VEC) filled with the last matching index, which we
can create using a MAX reduction and then expanding.
In the case where the loop never made any matches, the max index will
be zero. */
/* Vector of {0, 0, 0,...}. */
tree zero_vec = make_ssa_name (vectype);
tree zero_vec_rhs = build_zero_cst (vectype);
gimple *zero_vec_stmt = gimple_build_assign (zero_vec, zero_vec_rhs);
gsi_insert_before (&exit_gsi, zero_vec_stmt, GSI_SAME_STMT);
/* Find maximum value from the vector of found indexes. */
tree max_index = make_ssa_name (index_scalar_type);
gcall *max_index_stmt = gimple_build_call_internal (IFN_REDUC_MAX,
1, induction_index);
gimple_call_set_lhs (max_index_stmt, max_index);
gsi_insert_before (&exit_gsi, max_index_stmt, GSI_SAME_STMT);
/* Vector of {max_index, max_index, max_index,...}. */
tree max_index_vec = make_ssa_name (index_vec_type);
tree max_index_vec_rhs = build_vector_from_val (index_vec_type,
max_index);
gimple *max_index_vec_stmt = gimple_build_assign (max_index_vec,
max_index_vec_rhs);
gsi_insert_before (&exit_gsi, max_index_vec_stmt, GSI_SAME_STMT);
/* Next we compare the new vector (MAX_INDEX_VEC) full of max indexes
with the vector (INDUCTION_INDEX) of found indexes, choosing values
from the data vector (NEW_PHI_RESULT) for matches, 0 (ZERO_VEC)
otherwise. Only one value should match, resulting in a vector
(VEC_COND) with one data value and the rest zeros.
In the case where the loop never made any matches, every index will
match, resulting in a vector with all data values (which will all be
the default value). */
/* Compare the max index vector to the vector of found indexes to find
the position of the max value. */
tree vec_compare = make_ssa_name (index_vec_cmp_type);
gimple *vec_compare_stmt = gimple_build_assign (vec_compare, EQ_EXPR,
induction_index,
max_index_vec);
gsi_insert_before (&exit_gsi, vec_compare_stmt, GSI_SAME_STMT);
/* Use the compare to choose either values from the data vector or
zero. */
tree vec_cond = make_ssa_name (vectype);
gimple *vec_cond_stmt = gimple_build_assign (vec_cond, VEC_COND_EXPR,
vec_compare, new_phi_result,
zero_vec);
gsi_insert_before (&exit_gsi, vec_cond_stmt, GSI_SAME_STMT);
/* Finally we need to extract the data value from the vector (VEC_COND)
into a scalar (MATCHED_DATA_REDUC). Logically we want to do a OR
reduction, but because this doesn't exist, we can use a MAX reduction
instead. The data value might be signed or a float so we need to cast
it first.
In the case where the loop never made any matches, the data values are
all identical, and so will reduce down correctly. */
/* Make the matched data values unsigned. */
tree vec_cond_cast = make_ssa_name (vectype_unsigned);
tree vec_cond_cast_rhs = build1 (VIEW_CONVERT_EXPR, vectype_unsigned,
vec_cond);
gimple *vec_cond_cast_stmt = gimple_build_assign (vec_cond_cast,
VIEW_CONVERT_EXPR,
vec_cond_cast_rhs);
gsi_insert_before (&exit_gsi, vec_cond_cast_stmt, GSI_SAME_STMT);
/* Reduce down to a scalar value. */
tree data_reduc = make_ssa_name (scalar_type_unsigned);
gcall *data_reduc_stmt = gimple_build_call_internal (IFN_REDUC_MAX,
1, vec_cond_cast);
gimple_call_set_lhs (data_reduc_stmt, data_reduc);
gsi_insert_before (&exit_gsi, data_reduc_stmt, GSI_SAME_STMT);
/* Convert the reduced value back to the result type and set as the
result. */
gimple_seq stmts = NULL;
new_temp = gimple_build (&stmts, VIEW_CONVERT_EXPR, scalar_type,
data_reduc);
gsi_insert_seq_before (&exit_gsi, stmts, GSI_SAME_STMT);
scalar_results.safe_push (new_temp);
}
else if (STMT_VINFO_VEC_REDUCTION_TYPE (stmt_info) == COND_REDUCTION
&& reduc_fn == IFN_LAST)
{
/* Condition reduction without supported IFN_REDUC_MAX. Generate
idx = 0;
idx_val = induction_index[0];
val = data_reduc[0];
for (idx = 0, val = init, i = 0; i < nelts; ++i)
if (induction_index[i] > idx_val)
val = data_reduc[i], idx_val = induction_index[i];
return val; */
tree data_eltype = TREE_TYPE (TREE_TYPE (new_phi_result));
tree idx_eltype = TREE_TYPE (TREE_TYPE (induction_index));
unsigned HOST_WIDE_INT el_size = tree_to_uhwi (TYPE_SIZE (idx_eltype));
poly_uint64 nunits = TYPE_VECTOR_SUBPARTS (TREE_TYPE (induction_index));
/* Enforced by vectorizable_reduction, which ensures we have target
support before allowing a conditional reduction on variable-length
vectors. */
unsigned HOST_WIDE_INT v_size = el_size * nunits.to_constant ();
tree idx_val = NULL_TREE, val = NULL_TREE;
for (unsigned HOST_WIDE_INT off = 0; off < v_size; off += el_size)
{
tree old_idx_val = idx_val;
tree old_val = val;
idx_val = make_ssa_name (idx_eltype);
epilog_stmt = gimple_build_assign (idx_val, BIT_FIELD_REF,
build3 (BIT_FIELD_REF, idx_eltype,
induction_index,
bitsize_int (el_size),
bitsize_int (off)));
gsi_insert_before (&exit_gsi, epilog_stmt, GSI_SAME_STMT);
val = make_ssa_name (data_eltype);
epilog_stmt = gimple_build_assign (val, BIT_FIELD_REF,
build3 (BIT_FIELD_REF,
data_eltype,
new_phi_result,
bitsize_int (el_size),
bitsize_int (off)));
gsi_insert_before (&exit_gsi, epilog_stmt, GSI_SAME_STMT);
if (off != 0)
{
tree new_idx_val = idx_val;
tree new_val = val;
if (off != v_size - el_size)
{
new_idx_val = make_ssa_name (idx_eltype);
epilog_stmt = gimple_build_assign (new_idx_val,
MAX_EXPR, idx_val,
old_idx_val);
gsi_insert_before (&exit_gsi, epilog_stmt, GSI_SAME_STMT);
}
new_val = make_ssa_name (data_eltype);
epilog_stmt = gimple_build_assign (new_val,
COND_EXPR,
build2 (GT_EXPR,
boolean_type_node,
idx_val,
old_idx_val),
val, old_val);
gsi_insert_before (&exit_gsi, epilog_stmt, GSI_SAME_STMT);
idx_val = new_idx_val;
val = new_val;
}
}
/* Convert the reduced value back to the result type and set as the
result. */
gimple_seq stmts = NULL;
val = gimple_convert (&stmts, scalar_type, val);
gsi_insert_seq_before (&exit_gsi, stmts, GSI_SAME_STMT);
scalar_results.safe_push (val);
}
/* 2.3 Create the reduction code, using one of the three schemes described
above. In SLP we simply need to extract all the elements from the
vector (without reducing them), so we use scalar shifts. */
else if (reduc_fn != IFN_LAST && !slp_reduc)
{
tree tmp;
tree vec_elem_type;
/* Case 1: Create:
v_out2 = reduc_expr <v_out1> */
if (dump_enabled_p ())
dump_printf_loc (MSG_NOTE, vect_location,
"Reduce using direct vector reduction.\n");
vec_elem_type = TREE_TYPE (TREE_TYPE (new_phi_result));
if (!useless_type_conversion_p (scalar_type, vec_elem_type))
{
tree tmp_dest
= vect_create_destination_var (scalar_dest, vec_elem_type);
epilog_stmt = gimple_build_call_internal (reduc_fn, 1,
new_phi_result);
gimple_set_lhs (epilog_stmt, tmp_dest);
new_temp = make_ssa_name (tmp_dest, epilog_stmt);
gimple_set_lhs (epilog_stmt, new_temp);
gsi_insert_before (&exit_gsi, epilog_stmt, GSI_SAME_STMT);
epilog_stmt = gimple_build_assign (new_scalar_dest, NOP_EXPR,
new_temp);
}
else
{
epilog_stmt = gimple_build_call_internal (reduc_fn, 1,
new_phi_result);
gimple_set_lhs (epilog_stmt, new_scalar_dest);
}
new_temp = make_ssa_name (new_scalar_dest, epilog_stmt);
gimple_set_lhs (epilog_stmt, new_temp);
gsi_insert_before (&exit_gsi, epilog_stmt, GSI_SAME_STMT);
if ((STMT_VINFO_VEC_REDUCTION_TYPE (stmt_info)
== INTEGER_INDUC_COND_REDUCTION)
&& !operand_equal_p (initial_def, induc_val, 0))
{
/* Earlier we set the initial value to be a vector if induc_val
values. Check the result and if it is induc_val then replace
with the original initial value, unless induc_val is
the same as initial_def already. */
tree zcompare = build2 (EQ_EXPR, boolean_type_node, new_temp,
induc_val);
tmp = make_ssa_name (new_scalar_dest);
epilog_stmt = gimple_build_assign (tmp, COND_EXPR, zcompare,
initial_def, new_temp);
gsi_insert_before (&exit_gsi, epilog_stmt, GSI_SAME_STMT);
new_temp = tmp;
}
scalar_results.safe_push (new_temp);
}
else if (direct_slp_reduc)
{
/* Here we create one vector for each of the GROUP_SIZE results,
with the elements for other SLP statements replaced with the
neutral value. We can then do a normal reduction on each vector. */
/* Enforced by vectorizable_reduction. */
gcc_assert (new_phis.length () == 1);
gcc_assert (pow2p_hwi (group_size));
slp_tree orig_phis_slp_node = slp_node_instance->reduc_phis;
vec<gimple *> orig_phis = SLP_TREE_SCALAR_STMTS (orig_phis_slp_node);
gimple_seq seq = NULL;
/* Build a vector {0, 1, 2, ...}, with the same number of elements
and the same element size as VECTYPE. */
tree index = build_index_vector (vectype, 0, 1);
tree index_type = TREE_TYPE (index);
tree index_elt_type = TREE_TYPE (index_type);
tree mask_type = build_same_sized_truth_vector_type (index_type);
/* Create a vector that, for each element, identifies which of
the GROUP_SIZE results should use it. */
tree index_mask = build_int_cst (index_elt_type, group_size - 1);
index = gimple_build (&seq, BIT_AND_EXPR, index_type, index,
build_vector_from_val (index_type, index_mask));
/* Get a neutral vector value. This is simply a splat of the neutral
scalar value if we have one, otherwise the initial scalar value
is itself a neutral value. */
tree vector_identity = NULL_TREE;
if (neutral_op)
vector_identity = gimple_build_vector_from_val (&seq, vectype,
neutral_op);
for (unsigned int i = 0; i < group_size; ++i)
{
/* If there's no univeral neutral value, we can use the
initial scalar value from the original PHI. This is used
for MIN and MAX reduction, for example. */
if (!neutral_op)
{
tree scalar_value
= PHI_ARG_DEF_FROM_EDGE (orig_phis[i],
loop_preheader_edge (loop));
vector_identity = gimple_build_vector_from_val (&seq, vectype,
scalar_value);
}
/* Calculate the equivalent of:
sel[j] = (index[j] == i);
which selects the elements of NEW_PHI_RESULT that should
be included in the result. */
tree compare_val = build_int_cst (index_elt_type, i);
compare_val = build_vector_from_val (index_type, compare_val);
tree sel = gimple_build (&seq, EQ_EXPR, mask_type,
index, compare_val);
/* Calculate the equivalent of:
vec = seq ? new_phi_result : vector_identity;
VEC is now suitable for a full vector reduction. */
tree vec = gimple_build (&seq, VEC_COND_EXPR, vectype,
sel, new_phi_result, vector_identity);
/* Do the reduction and convert it to the appropriate type. */
gcall *call = gimple_build_call_internal (reduc_fn, 1, vec);
tree scalar = make_ssa_name (TREE_TYPE (vectype));
gimple_call_set_lhs (call, scalar);
gimple_seq_add_stmt (&seq, call);
scalar = gimple_convert (&seq, scalar_type, scalar);
scalar_results.safe_push (scalar);
}
gsi_insert_seq_before (&exit_gsi, seq, GSI_SAME_STMT);
}
else
{
bool reduce_with_shift;
tree vec_temp;
/* COND reductions all do the final reduction with MAX_EXPR
or MIN_EXPR. */
if (code == COND_EXPR)
{
if (STMT_VINFO_VEC_REDUCTION_TYPE (stmt_info)
== INTEGER_INDUC_COND_REDUCTION)
code = induc_code;
else
code = MAX_EXPR;
}
/* See if the target wants to do the final (shift) reduction
in a vector mode of smaller size and first reduce upper/lower
halves against each other. */
enum machine_mode mode1 = mode;
tree vectype1 = vectype;
unsigned sz = tree_to_uhwi (TYPE_SIZE_UNIT (vectype));
unsigned sz1 = sz;
if (!slp_reduc
&& (mode1 = targetm.vectorize.split_reduction (mode)) != mode)
sz1 = GET_MODE_SIZE (mode1).to_constant ();
vectype1 = get_vectype_for_scalar_type_and_size (scalar_type, sz1);
reduce_with_shift = have_whole_vector_shift (mode1);
if (!VECTOR_MODE_P (mode1))
reduce_with_shift = false;
else
{
optab optab = optab_for_tree_code (code, vectype1, optab_default);
if (optab_handler (optab, mode1) == CODE_FOR_nothing)
reduce_with_shift = false;
}
/* First reduce the vector to the desired vector size we should
do shift reduction on by combining upper and lower halves. */
new_temp = new_phi_result;
while (sz > sz1)
{
gcc_assert (!slp_reduc);
sz /= 2;
vectype1 = get_vectype_for_scalar_type_and_size (scalar_type, sz);
/* The target has to make sure we support lowpart/highpart
extraction, either via direct vector extract or through
an integer mode punning. */
tree dst1, dst2;
if (convert_optab_handler (vec_extract_optab,
TYPE_MODE (TREE_TYPE (new_temp)),
TYPE_MODE (vectype1))
!= CODE_FOR_nothing)
{
/* Extract sub-vectors directly once vec_extract becomes
a conversion optab. */
dst1 = make_ssa_name (vectype1);
epilog_stmt
= gimple_build_assign (dst1, BIT_FIELD_REF,
build3 (BIT_FIELD_REF, vectype1,
new_temp, TYPE_SIZE (vectype1),
bitsize_int (0)));
gsi_insert_before (&exit_gsi, epilog_stmt, GSI_SAME_STMT);
dst2 = make_ssa_name (vectype1);
epilog_stmt
= gimple_build_assign (dst2, BIT_FIELD_REF,
build3 (BIT_FIELD_REF, vectype1,
new_temp, TYPE_SIZE (vectype1),
bitsize_int (sz * BITS_PER_UNIT)));
gsi_insert_before (&exit_gsi, epilog_stmt, GSI_SAME_STMT);
}
else
{
/* Extract via punning to appropriately sized integer mode
vector. */
tree eltype = build_nonstandard_integer_type (sz * BITS_PER_UNIT,
1);
tree etype = build_vector_type (eltype, 2);
gcc_assert (convert_optab_handler (vec_extract_optab,
TYPE_MODE (etype),
TYPE_MODE (eltype))
!= CODE_FOR_nothing);
tree tem = make_ssa_name (etype);
epilog_stmt = gimple_build_assign (tem, VIEW_CONVERT_EXPR,
build1 (VIEW_CONVERT_EXPR,
etype, new_temp));
gsi_insert_before (&exit_gsi, epilog_stmt, GSI_SAME_STMT);
new_temp = tem;
tem = make_ssa_name (eltype);
epilog_stmt
= gimple_build_assign (tem, BIT_FIELD_REF,
build3 (BIT_FIELD_REF, eltype,
new_temp, TYPE_SIZE (eltype),
bitsize_int (0)));
gsi_insert_before (&exit_gsi, epilog_stmt, GSI_SAME_STMT);
dst1 = make_ssa_name (vectype1);
epilog_stmt = gimple_build_assign (dst1, VIEW_CONVERT_EXPR,
build1 (VIEW_CONVERT_EXPR,
vectype1, tem));
gsi_insert_before (&exit_gsi, epilog_stmt, GSI_SAME_STMT);
tem = make_ssa_name (eltype);
epilog_stmt
= gimple_build_assign (tem, BIT_FIELD_REF,
build3 (BIT_FIELD_REF, eltype,
new_temp, TYPE_SIZE (eltype),
bitsize_int (sz * BITS_PER_UNIT)));
gsi_insert_before (&exit_gsi, epilog_stmt, GSI_SAME_STMT);
dst2 = make_ssa_name (vectype1);
epilog_stmt = gimple_build_assign (dst2, VIEW_CONVERT_EXPR,
build1 (VIEW_CONVERT_EXPR,
vectype1, tem));
gsi_insert_before (&exit_gsi, epilog_stmt, GSI_SAME_STMT);
}
new_temp = make_ssa_name (vectype1);
epilog_stmt = gimple_build_assign (new_temp, code, dst1, dst2);
gsi_insert_before (&exit_gsi, epilog_stmt, GSI_SAME_STMT);
}
if (reduce_with_shift && !slp_reduc)
{
int element_bitsize = tree_to_uhwi (bitsize);
/* Enforced by vectorizable_reduction, which disallows SLP reductions
for variable-length vectors and also requires direct target support
for loop reductions. */
int vec_size_in_bits = tree_to_uhwi (TYPE_SIZE (vectype1));
int nelements = vec_size_in_bits / element_bitsize;
vec_perm_builder sel;
vec_perm_indices indices;
int elt_offset;
tree zero_vec = build_zero_cst (vectype1);
/* Case 2: Create:
for (offset = nelements/2; offset >= 1; offset/=2)
{
Create: va' = vec_shift <va, offset>
Create: va = vop <va, va'>
} */
tree rhs;
if (dump_enabled_p ())
dump_printf_loc (MSG_NOTE, vect_location,
"Reduce using vector shifts\n");
mode1 = TYPE_MODE (vectype1);
vec_dest = vect_create_destination_var (scalar_dest, vectype1);
for (elt_offset = nelements / 2;
elt_offset >= 1;
elt_offset /= 2)
{
calc_vec_perm_mask_for_shift (elt_offset, nelements, &sel);
indices.new_vector (sel, 2, nelements);
tree mask = vect_gen_perm_mask_any (vectype1, indices);
epilog_stmt = gimple_build_assign (vec_dest, VEC_PERM_EXPR,
new_temp, zero_vec, mask);
new_name = make_ssa_name (vec_dest, epilog_stmt);
gimple_assign_set_lhs (epilog_stmt, new_name);
gsi_insert_before (&exit_gsi, epilog_stmt, GSI_SAME_STMT);
epilog_stmt = gimple_build_assign (vec_dest, code, new_name,
new_temp);
new_temp = make_ssa_name (vec_dest, epilog_stmt);
gimple_assign_set_lhs (epilog_stmt, new_temp);
gsi_insert_before (&exit_gsi, epilog_stmt, GSI_SAME_STMT);
}
/* 2.4 Extract the final scalar result. Create:
s_out3 = extract_field <v_out2, bitpos> */
if (dump_enabled_p ())
dump_printf_loc (MSG_NOTE, vect_location,
"extract scalar result\n");
rhs = build3 (BIT_FIELD_REF, scalar_type, new_temp,
bitsize, bitsize_zero_node);
epilog_stmt = gimple_build_assign (new_scalar_dest, rhs);
new_temp = make_ssa_name (new_scalar_dest, epilog_stmt);
gimple_assign_set_lhs (epilog_stmt, new_temp);
gsi_insert_before (&exit_gsi, epilog_stmt, GSI_SAME_STMT);
scalar_results.safe_push (new_temp);
}
else
{
/* Case 3: Create:
s = extract_field <v_out2, 0>
for (offset = element_size;
offset < vector_size;
offset += element_size;)
{
Create: s' = extract_field <v_out2, offset>
Create: s = op <s, s'> // For non SLP cases
} */
if (dump_enabled_p ())
dump_printf_loc (MSG_NOTE, vect_location,
"Reduce using scalar code.\n");
int vec_size_in_bits = tree_to_uhwi (TYPE_SIZE (vectype1));
int element_bitsize = tree_to_uhwi (bitsize);
FOR_EACH_VEC_ELT (new_phis, i, new_phi)
{
int bit_offset;
if (gimple_code (new_phi) == GIMPLE_PHI)
vec_temp = PHI_RESULT (new_phi);
else
vec_temp = gimple_assign_lhs (new_phi);
tree rhs = build3 (BIT_FIELD_REF, scalar_type, vec_temp, bitsize,
bitsize_zero_node);
epilog_stmt = gimple_build_assign (new_scalar_dest, rhs);
new_temp = make_ssa_name (new_scalar_dest, epilog_stmt);
gimple_assign_set_lhs (epilog_stmt, new_temp);
gsi_insert_before (&exit_gsi, epilog_stmt, GSI_SAME_STMT);
/* In SLP we don't need to apply reduction operation, so we just
collect s' values in SCALAR_RESULTS. */
if (slp_reduc)
scalar_results.safe_push (new_temp);
for (bit_offset = element_bitsize;
bit_offset < vec_size_in_bits;
bit_offset += element_bitsize)
{
tree bitpos = bitsize_int (bit_offset);
tree rhs = build3 (BIT_FIELD_REF, scalar_type, vec_temp,
bitsize, bitpos);
epilog_stmt = gimple_build_assign (new_scalar_dest, rhs);
new_name = make_ssa_name (new_scalar_dest, epilog_stmt);
gimple_assign_set_lhs (epilog_stmt, new_name);
gsi_insert_before (&exit_gsi, epilog_stmt, GSI_SAME_STMT);
if (slp_reduc)
{
/* In SLP we don't need to apply reduction operation, so
we just collect s' values in SCALAR_RESULTS. */
new_temp = new_name;
scalar_results.safe_push (new_name);
}
else
{
epilog_stmt = gimple_build_assign (new_scalar_dest, code,
new_name, new_temp);
new_temp = make_ssa_name (new_scalar_dest, epilog_stmt);
gimple_assign_set_lhs (epilog_stmt, new_temp);
gsi_insert_before (&exit_gsi, epilog_stmt, GSI_SAME_STMT);
}
}
}
/* The only case where we need to reduce scalar results in SLP, is
unrolling. If the size of SCALAR_RESULTS is greater than
GROUP_SIZE, we reduce them combining elements modulo
GROUP_SIZE. */
if (slp_reduc)
{
tree res, first_res, new_res;
gimple *new_stmt;
/* Reduce multiple scalar results in case of SLP unrolling. */
for (j = group_size; scalar_results.iterate (j, &res);
j++)
{
first_res = scalar_results[j % group_size];
new_stmt = gimple_build_assign (new_scalar_dest, code,
first_res, res);
new_res = make_ssa_name (new_scalar_dest, new_stmt);
gimple_assign_set_lhs (new_stmt, new_res);
gsi_insert_before (&exit_gsi, new_stmt, GSI_SAME_STMT);
scalar_results[j % group_size] = new_res;
}
}
else
/* Not SLP - we have one scalar to keep in SCALAR_RESULTS. */
scalar_results.safe_push (new_temp);
}
if ((STMT_VINFO_VEC_REDUCTION_TYPE (stmt_info)
== INTEGER_INDUC_COND_REDUCTION)
&& !operand_equal_p (initial_def, induc_val, 0))
{
/* Earlier we set the initial value to be a vector if induc_val
values. Check the result and if it is induc_val then replace
with the original initial value, unless induc_val is
the same as initial_def already. */
tree zcompare = build2 (EQ_EXPR, boolean_type_node, new_temp,
induc_val);
tree tmp = make_ssa_name (new_scalar_dest);
epilog_stmt = gimple_build_assign (tmp, COND_EXPR, zcompare,
initial_def, new_temp);
gsi_insert_before (&exit_gsi, epilog_stmt, GSI_SAME_STMT);
scalar_results[0] = tmp;
}
}
vect_finalize_reduction:
if (double_reduc)
loop = loop->inner;
/* 2.5 Adjust the final result by the initial value of the reduction
variable. (When such adjustment is not needed, then
'adjustment_def' is zero). For example, if code is PLUS we create:
new_temp = loop_exit_def + adjustment_def */
if (adjustment_def)
{
gcc_assert (!slp_reduc);
if (nested_in_vect_loop)
{
new_phi = new_phis[0];
gcc_assert (TREE_CODE (TREE_TYPE (adjustment_def)) == VECTOR_TYPE);
expr = build2 (code, vectype, PHI_RESULT (new_phi), adjustment_def);
new_dest = vect_create_destination_var (scalar_dest, vectype);
}
else
{
new_temp = scalar_results[0];
gcc_assert (TREE_CODE (TREE_TYPE (adjustment_def)) != VECTOR_TYPE);
expr = build2 (code, scalar_type, new_temp, adjustment_def);
new_dest = vect_create_destination_var (scalar_dest, scalar_type);
}
epilog_stmt = gimple_build_assign (new_dest, expr);
new_temp = make_ssa_name (new_dest, epilog_stmt);
gimple_assign_set_lhs (epilog_stmt, new_temp);
gsi_insert_before (&exit_gsi, epilog_stmt, GSI_SAME_STMT);
if (nested_in_vect_loop)
{
set_vinfo_for_stmt (epilog_stmt,
new_stmt_vec_info (epilog_stmt, loop_vinfo));
STMT_VINFO_RELATED_STMT (vinfo_for_stmt (epilog_stmt)) =
STMT_VINFO_RELATED_STMT (vinfo_for_stmt (new_phi));
if (!double_reduc)
scalar_results.quick_push (new_temp);
else
scalar_results[0] = new_temp;
}
else
scalar_results[0] = new_temp;
new_phis[0] = epilog_stmt;
}
/* 2.6 Handle the loop-exit phis. Replace the uses of scalar loop-exit
phis with new adjusted scalar results, i.e., replace use <s_out0>
with use <s_out4>.
Transform:
loop_exit:
s_out0 = phi <s_loop> # (scalar) EXIT_PHI
v_out1 = phi <VECT_DEF> # NEW_EXIT_PHI
v_out2 = reduce <v_out1>
s_out3 = extract_field <v_out2, 0>
s_out4 = adjust_result <s_out3>
use <s_out0>
use <s_out0>
into:
loop_exit:
s_out0 = phi <s_loop> # (scalar) EXIT_PHI
v_out1 = phi <VECT_DEF> # NEW_EXIT_PHI
v_out2 = reduce <v_out1>
s_out3 = extract_field <v_out2, 0>
s_out4 = adjust_result <s_out3>
use <s_out4>
use <s_out4> */
/* In SLP reduction chain we reduce vector results into one vector if
necessary, hence we set here GROUP_SIZE to 1. SCALAR_DEST is the LHS of
the last stmt in the reduction chain, since we are looking for the loop
exit phi node. */
if (GROUP_FIRST_ELEMENT (vinfo_for_stmt (stmt)))
{
gimple *dest_stmt = SLP_TREE_SCALAR_STMTS (slp_node)[group_size - 1];
/* Handle reduction patterns. */
if (STMT_VINFO_RELATED_STMT (vinfo_for_stmt (dest_stmt)))
dest_stmt = STMT_VINFO_RELATED_STMT (vinfo_for_stmt (dest_stmt));
scalar_dest = gimple_assign_lhs (dest_stmt);
group_size = 1;
}
/* In SLP we may have several statements in NEW_PHIS and REDUCTION_PHIS (in
case that GROUP_SIZE is greater than vectorization factor). Therefore, we
need to match SCALAR_RESULTS with corresponding statements. The first
(GROUP_SIZE / number of new vector stmts) scalar results correspond to
the first vector stmt, etc.
(RATIO is equal to (GROUP_SIZE / number of new vector stmts)). */
if (group_size > new_phis.length ())
{
ratio = group_size / new_phis.length ();
gcc_assert (!(group_size % new_phis.length ()));
}
else
ratio = 1;
for (k = 0; k < group_size; k++)
{
if (k % ratio == 0)
{
epilog_stmt = new_phis[k / ratio];
reduction_phi = reduction_phis[k / ratio];
if (double_reduc)
inner_phi = inner_phis[k / ratio];
}
if (slp_reduc)
{
gimple *current_stmt = SLP_TREE_SCALAR_STMTS (slp_node)[k];
orig_stmt = STMT_VINFO_RELATED_STMT (vinfo_for_stmt (current_stmt));
/* SLP statements can't participate in patterns. */
gcc_assert (!orig_stmt);
scalar_dest = gimple_assign_lhs (current_stmt);
}
phis.create (3);
/* Find the loop-closed-use at the loop exit of the original scalar
result. (The reduction result is expected to have two immediate uses -
one at the latch block, and one at the loop exit). */
FOR_EACH_IMM_USE_FAST (use_p, imm_iter, scalar_dest)
if (!flow_bb_inside_loop_p (loop, gimple_bb (USE_STMT (use_p)))
&& !is_gimple_debug (USE_STMT (use_p)))
phis.safe_push (USE_STMT (use_p));
/* While we expect to have found an exit_phi because of loop-closed-ssa
form we can end up without one if the scalar cycle is dead. */
FOR_EACH_VEC_ELT (phis, i, exit_phi)
{
if (outer_loop)
{
stmt_vec_info exit_phi_vinfo = vinfo_for_stmt (exit_phi);
gphi *vect_phi;
/* FORNOW. Currently not supporting the case that an inner-loop
reduction is not used in the outer-loop (but only outside the
outer-loop), unless it is double reduction. */
gcc_assert ((STMT_VINFO_RELEVANT_P (exit_phi_vinfo)
&& !STMT_VINFO_LIVE_P (exit_phi_vinfo))
|| double_reduc);
if (double_reduc)
STMT_VINFO_VEC_STMT (exit_phi_vinfo) = inner_phi;
else
STMT_VINFO_VEC_STMT (exit_phi_vinfo) = epilog_stmt;
if (!double_reduc
|| STMT_VINFO_DEF_TYPE (exit_phi_vinfo)
!= vect_double_reduction_def)
continue;
/* Handle double reduction:
stmt1: s1 = phi <s0, s2> - double reduction phi (outer loop)
stmt2: s3 = phi <s1, s4> - (regular) reduc phi (inner loop)
stmt3: s4 = use (s3) - (regular) reduc stmt (inner loop)
stmt4: s2 = phi <s4> - double reduction stmt (outer loop)
At that point the regular reduction (stmt2 and stmt3) is
already vectorized, as well as the exit phi node, stmt4.
Here we vectorize the phi node of double reduction, stmt1, and
update all relevant statements. */
/* Go through all the uses of s2 to find double reduction phi
node, i.e., stmt1 above. */
orig_name = PHI_RESULT (exit_phi);
FOR_EACH_IMM_USE_STMT (use_stmt, imm_iter, orig_name)
{
stmt_vec_info use_stmt_vinfo;
stmt_vec_info new_phi_vinfo;
tree vect_phi_init, preheader_arg, vect_phi_res;
basic_block bb = gimple_bb (use_stmt);
gimple *use;
/* Check that USE_STMT is really double reduction phi
node. */
if (gimple_code (use_stmt) != GIMPLE_PHI
|| gimple_phi_num_args (use_stmt) != 2
|| bb->loop_father != outer_loop)
continue;
use_stmt_vinfo = vinfo_for_stmt (use_stmt);
if (!use_stmt_vinfo
|| STMT_VINFO_DEF_TYPE (use_stmt_vinfo)
!= vect_double_reduction_def)
continue;
/* Create vector phi node for double reduction:
vs1 = phi <vs0, vs2>
vs1 was created previously in this function by a call to
vect_get_vec_def_for_operand and is stored in
vec_initial_def;
vs2 is defined by INNER_PHI, the vectorized EXIT_PHI;
vs0 is created here. */
/* Create vector phi node. */
vect_phi = create_phi_node (vec_initial_def, bb);
new_phi_vinfo = new_stmt_vec_info (vect_phi,
loop_vec_info_for_loop (outer_loop));
set_vinfo_for_stmt (vect_phi, new_phi_vinfo);
/* Create vs0 - initial def of the double reduction phi. */
preheader_arg = PHI_ARG_DEF_FROM_EDGE (use_stmt,
loop_preheader_edge (outer_loop));
vect_phi_init = get_initial_def_for_reduction
(stmt, preheader_arg, NULL);
/* Update phi node arguments with vs0 and vs2. */
add_phi_arg (vect_phi, vect_phi_init,
loop_preheader_edge (outer_loop),
UNKNOWN_LOCATION);
add_phi_arg (vect_phi, PHI_RESULT (inner_phi),
loop_latch_edge (outer_loop), UNKNOWN_LOCATION);
if (dump_enabled_p ())
{
dump_printf_loc (MSG_NOTE, vect_location,
"created double reduction phi node: ");
dump_gimple_stmt (MSG_NOTE, TDF_SLIM, vect_phi, 0);
}
vect_phi_res = PHI_RESULT (vect_phi);
/* Replace the use, i.e., set the correct vs1 in the regular
reduction phi node. FORNOW, NCOPIES is always 1, so the
loop is redundant. */
use = reduction_phi;
for (j = 0; j < ncopies; j++)
{
edge pr_edge = loop_preheader_edge (loop);
SET_PHI_ARG_DEF (use, pr_edge->dest_idx, vect_phi_res);
use = STMT_VINFO_RELATED_STMT (vinfo_for_stmt (use));
}
}
}
}
phis.release ();
if (nested_in_vect_loop)
{
if (double_reduc)
loop = outer_loop;
else
continue;
}
phis.create (3);
/* Find the loop-closed-use at the loop exit of the original scalar
result. (The reduction result is expected to have two immediate uses,
one at the latch block, and one at the loop exit). For double
reductions we are looking for exit phis of the outer loop. */
FOR_EACH_IMM_USE_FAST (use_p, imm_iter, scalar_dest)
{
if (!flow_bb_inside_loop_p (loop, gimple_bb (USE_STMT (use_p))))
{
if (!is_gimple_debug (USE_STMT (use_p)))
phis.safe_push (USE_STMT (use_p));
}
else
{
if (double_reduc && gimple_code (USE_STMT (use_p)) == GIMPLE_PHI)
{
tree phi_res = PHI_RESULT (USE_STMT (use_p));
FOR_EACH_IMM_USE_FAST (phi_use_p, phi_imm_iter, phi_res)
{
if (!flow_bb_inside_loop_p (loop,
gimple_bb (USE_STMT (phi_use_p)))
&& !is_gimple_debug (USE_STMT (phi_use_p)))
phis.safe_push (USE_STMT (phi_use_p));
}
}
}
}
FOR_EACH_VEC_ELT (phis, i, exit_phi)
{
/* Replace the uses: */
orig_name = PHI_RESULT (exit_phi);
scalar_result = scalar_results[k];
FOR_EACH_IMM_USE_STMT (use_stmt, imm_iter, orig_name)
FOR_EACH_IMM_USE_ON_STMT (use_p, imm_iter)
SET_USE (use_p, scalar_result);
}
phis.release ();
}
}
/* Return a vector of type VECTYPE that is equal to the vector select
operation "MASK ? VEC : IDENTITY". Insert the select statements
before GSI. */
static tree
merge_with_identity (gimple_stmt_iterator *gsi, tree mask, tree vectype,
tree vec, tree identity)
{
tree cond = make_temp_ssa_name (vectype, NULL, "cond");
gimple *new_stmt = gimple_build_assign (cond, VEC_COND_EXPR,
mask, vec, identity);
gsi_insert_before (gsi, new_stmt, GSI_SAME_STMT);
return cond;
}
/* Successively apply CODE to each element of VECTOR_RHS, in left-to-right
order, starting with LHS. Insert the extraction statements before GSI and
associate the new scalar SSA names with variable SCALAR_DEST.
Return the SSA name for the result. */
static tree
vect_expand_fold_left (gimple_stmt_iterator *gsi, tree scalar_dest,
tree_code code, tree lhs, tree vector_rhs)
{
tree vectype = TREE_TYPE (vector_rhs);
tree scalar_type = TREE_TYPE (vectype);
tree bitsize = TYPE_SIZE (scalar_type);
unsigned HOST_WIDE_INT vec_size_in_bits = tree_to_uhwi (TYPE_SIZE (vectype));
unsigned HOST_WIDE_INT element_bitsize = tree_to_uhwi (bitsize);
for (unsigned HOST_WIDE_INT bit_offset = 0;
bit_offset < vec_size_in_bits;
bit_offset += element_bitsize)
{
tree bitpos = bitsize_int (bit_offset);
tree rhs = build3 (BIT_FIELD_REF, scalar_type, vector_rhs,
bitsize, bitpos);
gassign *stmt = gimple_build_assign (scalar_dest, rhs);
rhs = make_ssa_name (scalar_dest, stmt);
gimple_assign_set_lhs (stmt, rhs);
gsi_insert_before (gsi, stmt, GSI_SAME_STMT);
stmt = gimple_build_assign (scalar_dest, code, lhs, rhs);
tree new_name = make_ssa_name (scalar_dest, stmt);
gimple_assign_set_lhs (stmt, new_name);
gsi_insert_before (gsi, stmt, GSI_SAME_STMT);
lhs = new_name;
}
return lhs;
}
/* Perform an in-order reduction (FOLD_LEFT_REDUCTION). STMT is the
statement that sets the live-out value. REDUC_DEF_STMT is the phi
statement. CODE is the operation performed by STMT and OPS are
its scalar operands. REDUC_INDEX is the index of the operand in
OPS that is set by REDUC_DEF_STMT. REDUC_FN is the function that
implements in-order reduction, or IFN_LAST if we should open-code it.
VECTYPE_IN is the type of the vector input. MASKS specifies the masks
that should be used to control the operation in a fully-masked loop. */
static bool
vectorize_fold_left_reduction (gimple *stmt, gimple_stmt_iterator *gsi,
gimple **vec_stmt, slp_tree slp_node,
gimple *reduc_def_stmt,
tree_code code, internal_fn reduc_fn,
tree ops[3], tree vectype_in,
int reduc_index, vec_loop_masks *masks)
{
stmt_vec_info stmt_info = vinfo_for_stmt (stmt);
loop_vec_info loop_vinfo = STMT_VINFO_LOOP_VINFO (stmt_info);
struct loop *loop = LOOP_VINFO_LOOP (loop_vinfo);
tree vectype_out = STMT_VINFO_VECTYPE (stmt_info);
gimple *new_stmt = NULL;
int ncopies;
if (slp_node)
ncopies = 1;
else
ncopies = vect_get_num_copies (loop_vinfo, vectype_in);
gcc_assert (!nested_in_vect_loop_p (loop, stmt));
gcc_assert (ncopies == 1);
gcc_assert (TREE_CODE_LENGTH (code) == binary_op);
gcc_assert (reduc_index == (code == MINUS_EXPR ? 0 : 1));
gcc_assert (STMT_VINFO_VEC_REDUCTION_TYPE (stmt_info)
== FOLD_LEFT_REDUCTION);
if (slp_node)
gcc_assert (known_eq (TYPE_VECTOR_SUBPARTS (vectype_out),
TYPE_VECTOR_SUBPARTS (vectype_in)));
tree op0 = ops[1 - reduc_index];
int group_size = 1;
gimple *scalar_dest_def;
auto_vec<tree> vec_oprnds0;
if (slp_node)
{
vect_get_vec_defs (op0, NULL_TREE, stmt, &vec_oprnds0, NULL, slp_node);
group_size = SLP_TREE_SCALAR_STMTS (slp_node).length ();
scalar_dest_def = SLP_TREE_SCALAR_STMTS (slp_node)[group_size - 1];
}
else
{
tree loop_vec_def0 = vect_get_vec_def_for_operand (op0, stmt);
vec_oprnds0.create (1);
vec_oprnds0.quick_push (loop_vec_def0);
scalar_dest_def = stmt;
}
tree scalar_dest = gimple_assign_lhs (scalar_dest_def);
tree scalar_type = TREE_TYPE (scalar_dest);
tree reduc_var = gimple_phi_result (reduc_def_stmt);
int vec_num = vec_oprnds0.length ();
gcc_assert (vec_num == 1 || slp_node);
tree vec_elem_type = TREE_TYPE (vectype_out);
gcc_checking_assert (useless_type_conversion_p (scalar_type, vec_elem_type));
tree vector_identity = NULL_TREE;
if (LOOP_VINFO_FULLY_MASKED_P (loop_vinfo))
vector_identity = build_zero_cst (vectype_out);
tree scalar_dest_var = vect_create_destination_var (scalar_dest, NULL);
int i;
tree def0;
FOR_EACH_VEC_ELT (vec_oprnds0, i, def0)
{
tree mask = NULL_TREE;
if (LOOP_VINFO_FULLY_MASKED_P (loop_vinfo))
mask = vect_get_loop_mask (gsi, masks, vec_num, vectype_in, i);
/* Handle MINUS by adding the negative. */
if (reduc_fn != IFN_LAST && code == MINUS_EXPR)
{
tree negated = make_ssa_name (vectype_out);
new_stmt = gimple_build_assign (negated, NEGATE_EXPR, def0);
gsi_insert_before (gsi, new_stmt, GSI_SAME_STMT);
def0 = negated;
}
if (mask)
def0 = merge_with_identity (gsi, mask, vectype_out, def0,
vector_identity);
/* On the first iteration the input is simply the scalar phi
result, and for subsequent iterations it is the output of
the preceding operation. */
if (reduc_fn != IFN_LAST)
{
new_stmt = gimple_build_call_internal (reduc_fn, 2, reduc_var, def0);
/* For chained SLP reductions the output of the previous reduction
operation serves as the input of the next. For the final statement
the output cannot be a temporary - we reuse the original
scalar destination of the last statement. */
if (i != vec_num - 1)
{
gimple_set_lhs (new_stmt, scalar_dest_var);
reduc_var = make_ssa_name (scalar_dest_var, new_stmt);
gimple_set_lhs (new_stmt, reduc_var);
}
}
else
{
reduc_var = vect_expand_fold_left (gsi, scalar_dest_var, code,
reduc_var, def0);
new_stmt = SSA_NAME_DEF_STMT (reduc_var);
/* Remove the statement, so that we can use the same code paths
as for statements that we've just created. */
gimple_stmt_iterator tmp_gsi = gsi_for_stmt (new_stmt);
gsi_remove (&tmp_gsi, false);
}
if (i == vec_num - 1)
{
gimple_set_lhs (new_stmt, scalar_dest);
vect_finish_replace_stmt (scalar_dest_def, new_stmt);
}
else
vect_finish_stmt_generation (scalar_dest_def, new_stmt, gsi);
if (slp_node)
SLP_TREE_VEC_STMTS (slp_node).quick_push (new_stmt);
}
if (!slp_node)
STMT_VINFO_VEC_STMT (stmt_info) = *vec_stmt = new_stmt;
return true;
}
/* Function is_nonwrapping_integer_induction.
Check if STMT (which is part of loop LOOP) both increments and
does not cause overflow. */
static bool
is_nonwrapping_integer_induction (gimple *stmt, struct loop *loop)
{
stmt_vec_info stmt_vinfo = vinfo_for_stmt (stmt);
tree base = STMT_VINFO_LOOP_PHI_EVOLUTION_BASE_UNCHANGED (stmt_vinfo);
tree step = STMT_VINFO_LOOP_PHI_EVOLUTION_PART (stmt_vinfo);
tree lhs_type = TREE_TYPE (gimple_phi_result (stmt));
widest_int ni, max_loop_value, lhs_max;
bool overflow = false;
/* Make sure the loop is integer based. */
if (TREE_CODE (base) != INTEGER_CST
|| TREE_CODE (step) != INTEGER_CST)
return false;
/* Check that the max size of the loop will not wrap. */
if (TYPE_OVERFLOW_UNDEFINED (lhs_type))
return true;
if (! max_stmt_executions (loop, &ni))
return false;
max_loop_value = wi::mul (wi::to_widest (step), ni, TYPE_SIGN (lhs_type),
&overflow);
if (overflow)
return false;
max_loop_value = wi::add (wi::to_widest (base), max_loop_value,
TYPE_SIGN (lhs_type), &overflow);
if (overflow)
return false;
return (wi::min_precision (max_loop_value, TYPE_SIGN (lhs_type))
<= TYPE_PRECISION (lhs_type));
}
/* Function vectorizable_reduction.
Check if STMT performs a reduction operation that can be vectorized.
If VEC_STMT is also passed, vectorize the STMT: create a vectorized
stmt to replace it, put it in VEC_STMT, and insert it at GSI.
Return FALSE if not a vectorizable STMT, TRUE otherwise.
This function also handles reduction idioms (patterns) that have been
recognized in advance during vect_pattern_recog. In this case, STMT may be
of this form:
X = pattern_expr (arg0, arg1, ..., X)
and it's STMT_VINFO_RELATED_STMT points to the last stmt in the original
sequence that had been detected and replaced by the pattern-stmt (STMT).
This function also handles reduction of condition expressions, for example:
for (int i = 0; i < N; i++)
if (a[i] < value)
last = a[i];
This is handled by vectorising the loop and creating an additional vector
containing the loop indexes for which "a[i] < value" was true. In the
function epilogue this is reduced to a single max value and then used to
index into the vector of results.
In some cases of reduction patterns, the type of the reduction variable X is
different than the type of the other arguments of STMT.
In such cases, the vectype that is used when transforming STMT into a vector
stmt is different than the vectype that is used to determine the
vectorization factor, because it consists of a different number of elements
than the actual number of elements that are being operated upon in parallel.
For example, consider an accumulation of shorts into an int accumulator.
On some targets it's possible to vectorize this pattern operating on 8
shorts at a time (hence, the vectype for purposes of determining the
vectorization factor should be V8HI); on the other hand, the vectype that
is used to create the vector form is actually V4SI (the type of the result).
Upon entry to this function, STMT_VINFO_VECTYPE records the vectype that
indicates what is the actual level of parallelism (V8HI in the example), so
that the right vectorization factor would be derived. This vectype
corresponds to the type of arguments to the reduction stmt, and should *NOT*
be used to create the vectorized stmt. The right vectype for the vectorized
stmt is obtained from the type of the result X:
get_vectype_for_scalar_type (TREE_TYPE (X))
This means that, contrary to "regular" reductions (or "regular" stmts in
general), the following equation:
STMT_VINFO_VECTYPE == get_vectype_for_scalar_type (TREE_TYPE (X))
does *NOT* necessarily hold for reduction patterns. */
bool
vectorizable_reduction (gimple *stmt, gimple_stmt_iterator *gsi,
gimple **vec_stmt, slp_tree slp_node,
slp_instance slp_node_instance)
{
tree vec_dest;
tree scalar_dest;
stmt_vec_info stmt_info = vinfo_for_stmt (stmt);
tree vectype_out = STMT_VINFO_VECTYPE (stmt_info);
tree vectype_in = NULL_TREE;
loop_vec_info loop_vinfo = STMT_VINFO_LOOP_VINFO (stmt_info);
struct loop *loop = LOOP_VINFO_LOOP (loop_vinfo);
enum tree_code code, orig_code;
internal_fn reduc_fn;
machine_mode vec_mode;
int op_type;
optab optab;
tree new_temp = NULL_TREE;
gimple *def_stmt;
enum vect_def_type dt, cond_reduc_dt = vect_unknown_def_type;
gimple *cond_reduc_def_stmt = NULL;
enum tree_code cond_reduc_op_code = ERROR_MARK;
tree scalar_type;
bool is_simple_use;
gimple *orig_stmt;
stmt_vec_info orig_stmt_info = NULL;
int i;
int ncopies;
int epilog_copies;
stmt_vec_info prev_stmt_info, prev_phi_info;
bool single_defuse_cycle = false;
gimple *new_stmt = NULL;
int j;
tree ops[3];
enum vect_def_type dts[3];
bool nested_cycle = false, found_nested_cycle_def = false;
bool double_reduc = false;
basic_block def_bb;
struct loop * def_stmt_loop, *outer_loop = NULL;
tree def_arg;
gimple *def_arg_stmt;
auto_vec<tree> vec_oprnds0;
auto_vec<tree> vec_oprnds1;
auto_vec<tree> vec_oprnds2;
auto_vec<tree> vect_defs;
auto_vec<gimple *> phis;
int vec_num;
tree def0, tem;
bool first_p = true;
tree cr_index_scalar_type = NULL_TREE, cr_index_vector_type = NULL_TREE;
tree cond_reduc_val = NULL_TREE;
/* Make sure it was already recognized as a reduction computation. */
if (STMT_VINFO_DEF_TYPE (vinfo_for_stmt (stmt)) != vect_reduction_def
&& STMT_VINFO_DEF_TYPE (vinfo_for_stmt (stmt)) != vect_nested_cycle)
return false;
if (nested_in_vect_loop_p (loop, stmt))
{
outer_loop = loop;
loop = loop->inner;
nested_cycle = true;
}
/* In case of reduction chain we switch to the first stmt in the chain, but
we don't update STMT_INFO, since only the last stmt is marked as reduction
and has reduction properties. */
if (GROUP_FIRST_ELEMENT (stmt_info)
&& GROUP_FIRST_ELEMENT (stmt_info) != stmt)
{
stmt = GROUP_FIRST_ELEMENT (stmt_info);
first_p = false;
}
if (gimple_code (stmt) == GIMPLE_PHI)
{
/* Analysis is fully done on the reduction stmt invocation. */
if (! vec_stmt)
{
if (slp_node)
slp_node_instance->reduc_phis = slp_node;
STMT_VINFO_TYPE (stmt_info) = reduc_vec_info_type;
return true;
}
if (STMT_VINFO_REDUC_TYPE (stmt_info) == FOLD_LEFT_REDUCTION)
/* Leave the scalar phi in place. Note that checking
STMT_VINFO_VEC_REDUCTION_TYPE (as below) only works
for reductions involving a single statement. */
return true;
gimple *reduc_stmt = STMT_VINFO_REDUC_DEF (stmt_info);
if (STMT_VINFO_IN_PATTERN_P (vinfo_for_stmt (reduc_stmt)))
reduc_stmt = STMT_VINFO_RELATED_STMT (vinfo_for_stmt (reduc_stmt));
if (STMT_VINFO_VEC_REDUCTION_TYPE (vinfo_for_stmt (reduc_stmt))
== EXTRACT_LAST_REDUCTION)
/* Leave the scalar phi in place. */
return true;
gcc_assert (is_gimple_assign (reduc_stmt));
for (unsigned k = 1; k < gimple_num_ops (reduc_stmt); ++k)
{
tree op = gimple_op (reduc_stmt, k);
if (op == gimple_phi_result (stmt))
continue;
if (k == 1
&& gimple_assign_rhs_code (reduc_stmt) == COND_EXPR)
continue;
if (!vectype_in
|| (GET_MODE_SIZE (SCALAR_TYPE_MODE (TREE_TYPE (vectype_in)))
< GET_MODE_SIZE (SCALAR_TYPE_MODE (TREE_TYPE (op)))))
vectype_in = get_vectype_for_scalar_type (TREE_TYPE (op));
break;
}
gcc_assert (vectype_in);
if (slp_node)
ncopies = 1;
else
ncopies = vect_get_num_copies (loop_vinfo, vectype_in);
use_operand_p use_p;
gimple *use_stmt;
if (ncopies > 1
&& (STMT_VINFO_RELEVANT (vinfo_for_stmt (reduc_stmt))
<= vect_used_only_live)
&& single_imm_use (gimple_phi_result (stmt), &use_p, &use_stmt)
&& (use_stmt == reduc_stmt
|| (STMT_VINFO_RELATED_STMT (vinfo_for_stmt (use_stmt))
== reduc_stmt)))
single_defuse_cycle = true;
/* Create the destination vector */
scalar_dest = gimple_assign_lhs (reduc_stmt);
vec_dest = vect_create_destination_var (scalar_dest, vectype_out);
if (slp_node)
/* The size vect_schedule_slp_instance computes is off for us. */
vec_num = vect_get_num_vectors
(LOOP_VINFO_VECT_FACTOR (loop_vinfo)
* SLP_TREE_SCALAR_STMTS (slp_node).length (),
vectype_in);
else
vec_num = 1;
/* Generate the reduction PHIs upfront. */
prev_phi_info = NULL;
for (j = 0; j < ncopies; j++)
{
if (j == 0 || !single_defuse_cycle)
{
for (i = 0; i < vec_num; i++)
{
/* Create the reduction-phi that defines the reduction
operand. */
gimple *new_phi = create_phi_node (vec_dest, loop->header);
set_vinfo_for_stmt (new_phi,
new_stmt_vec_info (new_phi, loop_vinfo));
if (slp_node)
SLP_TREE_VEC_STMTS (slp_node).quick_push (new_phi);
else
{
if (j == 0)
STMT_VINFO_VEC_STMT (stmt_info) = *vec_stmt = new_phi;
else
STMT_VINFO_RELATED_STMT (prev_phi_info) = new_phi;
prev_phi_info = vinfo_for_stmt (new_phi);
}
}
}
}
return true;
}
/* 1. Is vectorizable reduction? */
/* Not supportable if the reduction variable is used in the loop, unless
it's a reduction chain. */
if (STMT_VINFO_RELEVANT (stmt_info) > vect_used_in_outer
&& !GROUP_FIRST_ELEMENT (stmt_info))
return false;
/* Reductions that are not used even in an enclosing outer-loop,
are expected to be "live" (used out of the loop). */
if (STMT_VINFO_RELEVANT (stmt_info) == vect_unused_in_scope
&& !STMT_VINFO_LIVE_P (stmt_info))
return false;
/* 2. Has this been recognized as a reduction pattern?
Check if STMT represents a pattern that has been recognized
in earlier analysis stages. For stmts that represent a pattern,
the STMT_VINFO_RELATED_STMT field records the last stmt in
the original sequence that constitutes the pattern. */
orig_stmt = STMT_VINFO_RELATED_STMT (vinfo_for_stmt (stmt));
if (orig_stmt)
{
orig_stmt_info = vinfo_for_stmt (orig_stmt);
gcc_assert (STMT_VINFO_IN_PATTERN_P (orig_stmt_info));
gcc_assert (!STMT_VINFO_IN_PATTERN_P (stmt_info));
}
/* 3. Check the operands of the operation. The first operands are defined
inside the loop body. The last operand is the reduction variable,
which is defined by the loop-header-phi. */
gcc_assert (is_gimple_assign (stmt));
/* Flatten RHS. */
switch (get_gimple_rhs_class (gimple_assign_rhs_code (stmt)))
{
case GIMPLE_BINARY_RHS:
code = gimple_assign_rhs_code (stmt);
op_type = TREE_CODE_LENGTH (code);
gcc_assert (op_type == binary_op);
ops[0] = gimple_assign_rhs1 (stmt);
ops[1] = gimple_assign_rhs2 (stmt);
break;
case GIMPLE_TERNARY_RHS:
code = gimple_assign_rhs_code (stmt);
op_type = TREE_CODE_LENGTH (code);
gcc_assert (op_type == ternary_op);
ops[0] = gimple_assign_rhs1 (stmt);
ops[1] = gimple_assign_rhs2 (stmt);
ops[2] = gimple_assign_rhs3 (stmt);
break;
case GIMPLE_UNARY_RHS:
return false;
default:
gcc_unreachable ();
}
if (code == COND_EXPR && slp_node)
return false;
scalar_dest = gimple_assign_lhs (stmt);
scalar_type = TREE_TYPE (scalar_dest);
if (!POINTER_TYPE_P (scalar_type) && !INTEGRAL_TYPE_P (scalar_type)
&& !SCALAR_FLOAT_TYPE_P (scalar_type))
return false;
/* Do not try to vectorize bit-precision reductions. */
if (!type_has_mode_precision_p (scalar_type))
return false;
/* All uses but the last are expected to be defined in the loop.
The last use is the reduction variable. In case of nested cycle this
assumption is not true: we use reduc_index to record the index of the
reduction variable. */
gimple *reduc_def_stmt = NULL;
int reduc_index = -1;
for (i = 0; i < op_type; i++)
{
/* The condition of COND_EXPR is checked in vectorizable_condition(). */
if (i == 0 && code == COND_EXPR)
continue;
is_simple_use = vect_is_simple_use (ops[i], loop_vinfo,
&def_stmt, &dts[i], &tem);
dt = dts[i];
gcc_assert (is_simple_use);
if (dt == vect_reduction_def)
{
reduc_def_stmt = def_stmt;
reduc_index = i;
continue;
}
else if (tem)
{
/* To properly compute ncopies we are interested in the widest
input type in case we're looking at a widening accumulation. */
if (!vectype_in
|| (GET_MODE_SIZE (SCALAR_TYPE_MODE (TREE_TYPE (vectype_in)))
< GET_MODE_SIZE (SCALAR_TYPE_MODE (TREE_TYPE (tem)))))
vectype_in = tem;
}
if (dt != vect_internal_def
&& dt != vect_external_def
&& dt != vect_constant_def
&& dt != vect_induction_def
&& !(dt == vect_nested_cycle && nested_cycle))
return false;
if (dt == vect_nested_cycle)
{
found_nested_cycle_def = true;
reduc_def_stmt = def_stmt;
reduc_index = i;
}
if (i == 1 && code == COND_EXPR)
{
/* Record how value of COND_EXPR is defined. */
if (dt == vect_constant_def)
{
cond_reduc_dt = dt;
cond_reduc_val = ops[i];
}
if (dt == vect_induction_def
&& def_stmt != NULL
&& is_nonwrapping_integer_induction (def_stmt, loop))
{
cond_reduc_dt = dt;
cond_reduc_def_stmt = def_stmt;
}
}
}
if (!vectype_in)
vectype_in = vectype_out;
/* When vectorizing a reduction chain w/o SLP the reduction PHI is not
directy used in stmt. */
if (reduc_index == -1)
{
if (STMT_VINFO_REDUC_TYPE (stmt_info) == FOLD_LEFT_REDUCTION)
{
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"in-order reduction chain without SLP.\n");
return false;
}
if (orig_stmt)
reduc_def_stmt = STMT_VINFO_REDUC_DEF (orig_stmt_info);
else
reduc_def_stmt = STMT_VINFO_REDUC_DEF (stmt_info);
}
if (! reduc_def_stmt || gimple_code (reduc_def_stmt) != GIMPLE_PHI)
return false;
if (!(reduc_index == -1
|| dts[reduc_index] == vect_reduction_def
|| dts[reduc_index] == vect_nested_cycle
|| ((dts[reduc_index] == vect_internal_def
|| dts[reduc_index] == vect_external_def
|| dts[reduc_index] == vect_constant_def
|| dts[reduc_index] == vect_induction_def)
&& nested_cycle && found_nested_cycle_def)))
{
/* For pattern recognized stmts, orig_stmt might be a reduction,
but some helper statements for the pattern might not, or
might be COND_EXPRs with reduction uses in the condition. */
gcc_assert (orig_stmt);
return false;
}
stmt_vec_info reduc_def_info = vinfo_for_stmt (reduc_def_stmt);
enum vect_reduction_type v_reduc_type
= STMT_VINFO_REDUC_TYPE (reduc_def_info);
gimple *tmp = STMT_VINFO_REDUC_DEF (reduc_def_info);
STMT_VINFO_VEC_REDUCTION_TYPE (stmt_info) = v_reduc_type;
/* If we have a condition reduction, see if we can simplify it further. */
if (v_reduc_type == COND_REDUCTION)
{
/* TODO: We can't yet handle reduction chains, since we need to treat
each COND_EXPR in the chain specially, not just the last one.
E.g. for:
x_1 = PHI <x_3, ...>
x_2 = a_2 ? ... : x_1;
x_3 = a_3 ? ... : x_2;
we're interested in the last element in x_3 for which a_2 || a_3
is true, whereas the current reduction chain handling would
vectorize x_2 as a normal VEC_COND_EXPR and only treat x_3
as a reduction operation. */
if (reduc_index == -1)
{
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"conditional reduction chains not supported\n");
return false;
}
/* vect_is_simple_reduction ensured that operand 2 is the
loop-carried operand. */
gcc_assert (reduc_index == 2);
/* Loop peeling modifies initial value of reduction PHI, which
makes the reduction stmt to be transformed different to the
original stmt analyzed. We need to record reduction code for
CONST_COND_REDUCTION type reduction at analyzing stage, thus
it can be used directly at transform stage. */
if (STMT_VINFO_VEC_CONST_COND_REDUC_CODE (stmt_info) == MAX_EXPR
|| STMT_VINFO_VEC_CONST_COND_REDUC_CODE (stmt_info) == MIN_EXPR)
{
/* Also set the reduction type to CONST_COND_REDUCTION. */
gcc_assert (cond_reduc_dt == vect_constant_def);
STMT_VINFO_VEC_REDUCTION_TYPE (stmt_info) = CONST_COND_REDUCTION;
}
else if (direct_internal_fn_supported_p (IFN_FOLD_EXTRACT_LAST,
vectype_in, OPTIMIZE_FOR_SPEED))
{
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"optimizing condition reduction with"
" FOLD_EXTRACT_LAST.\n");
STMT_VINFO_VEC_REDUCTION_TYPE (stmt_info) = EXTRACT_LAST_REDUCTION;
}
else if (cond_reduc_dt == vect_induction_def)
{
stmt_vec_info cond_stmt_vinfo = vinfo_for_stmt (cond_reduc_def_stmt);
tree base
= STMT_VINFO_LOOP_PHI_EVOLUTION_BASE_UNCHANGED (cond_stmt_vinfo);
tree step = STMT_VINFO_LOOP_PHI_EVOLUTION_PART (cond_stmt_vinfo);
gcc_assert (TREE_CODE (base) == INTEGER_CST
&& TREE_CODE (step) == INTEGER_CST);
cond_reduc_val = NULL_TREE;
/* Find a suitable value, for MAX_EXPR below base, for MIN_EXPR
above base; punt if base is the minimum value of the type for
MAX_EXPR or maximum value of the type for MIN_EXPR for now. */
if (tree_int_cst_sgn (step) == -1)
{
cond_reduc_op_code = MIN_EXPR;
if (tree_int_cst_sgn (base) == -1)
cond_reduc_val = build_int_cst (TREE_TYPE (base), 0);
else if (tree_int_cst_lt (base,
TYPE_MAX_VALUE (TREE_TYPE (base))))
cond_reduc_val
= int_const_binop (PLUS_EXPR, base, integer_one_node);
}
else
{
cond_reduc_op_code = MAX_EXPR;
if (tree_int_cst_sgn (base) == 1)
cond_reduc_val = build_int_cst (TREE_TYPE (base), 0);
else if (tree_int_cst_lt (TYPE_MIN_VALUE (TREE_TYPE (base)),
base))
cond_reduc_val
= int_const_binop (MINUS_EXPR, base, integer_one_node);
}
if (cond_reduc_val)
{
if (dump_enabled_p ())
dump_printf_loc (MSG_NOTE, vect_location,
"condition expression based on "
"integer induction.\n");
STMT_VINFO_VEC_REDUCTION_TYPE (stmt_info)
= INTEGER_INDUC_COND_REDUCTION;
}
}
else if (cond_reduc_dt == vect_constant_def)
{
enum vect_def_type cond_initial_dt;
gimple *def_stmt = SSA_NAME_DEF_STMT (ops[reduc_index]);
tree cond_initial_val
= PHI_ARG_DEF_FROM_EDGE (def_stmt, loop_preheader_edge (loop));
gcc_assert (cond_reduc_val != NULL_TREE);
vect_is_simple_use (cond_initial_val, loop_vinfo,
&def_stmt, &cond_initial_dt);
if (cond_initial_dt == vect_constant_def
&& types_compatible_p (TREE_TYPE (cond_initial_val),
TREE_TYPE (cond_reduc_val)))
{
tree e = fold_binary (LE_EXPR, boolean_type_node,
cond_initial_val, cond_reduc_val);
if (e && (integer_onep (e) || integer_zerop (e)))
{
if (dump_enabled_p ())
dump_printf_loc (MSG_NOTE, vect_location,
"condition expression based on "
"compile time constant.\n");
/* Record reduction code at analysis stage. */
STMT_VINFO_VEC_CONST_COND_REDUC_CODE (stmt_info)
= integer_onep (e) ? MAX_EXPR : MIN_EXPR;
STMT_VINFO_VEC_REDUCTION_TYPE (stmt_info)
= CONST_COND_REDUCTION;
}
}
}
}
if (orig_stmt)
gcc_assert (tmp == orig_stmt
|| GROUP_FIRST_ELEMENT (vinfo_for_stmt (tmp)) == orig_stmt);
else
/* We changed STMT to be the first stmt in reduction chain, hence we
check that in this case the first element in the chain is STMT. */
gcc_assert (stmt == tmp
|| GROUP_FIRST_ELEMENT (vinfo_for_stmt (tmp)) == stmt);
if (STMT_VINFO_LIVE_P (vinfo_for_stmt (reduc_def_stmt)))
return false;
if (slp_node)
ncopies = 1;
else
ncopies = vect_get_num_copies (loop_vinfo, vectype_in);
gcc_assert (ncopies >= 1);
vec_mode = TYPE_MODE (vectype_in);
poly_uint64 nunits_out = TYPE_VECTOR_SUBPARTS (vectype_out);
if (code == COND_EXPR)
{
/* Only call during the analysis stage, otherwise we'll lose
STMT_VINFO_TYPE. */
if (!vec_stmt && !vectorizable_condition (stmt, gsi, NULL,
ops[reduc_index], 0, NULL))
{
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"unsupported condition in reduction\n");
return false;
}
}
else
{
/* 4. Supportable by target? */
if (code == LSHIFT_EXPR || code == RSHIFT_EXPR
|| code == LROTATE_EXPR || code == RROTATE_EXPR)
{
/* Shifts and rotates are only supported by vectorizable_shifts,
not vectorizable_reduction. */
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"unsupported shift or rotation.\n");
return false;
}
/* 4.1. check support for the operation in the loop */
optab = optab_for_tree_code (code, vectype_in, optab_default);
if (!optab)
{
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"no optab.\n");
return false;
}
if (optab_handler (optab, vec_mode) == CODE_FOR_nothing)
{
if (dump_enabled_p ())
dump_printf (MSG_NOTE, "op not supported by target.\n");
if (maybe_ne (GET_MODE_SIZE (vec_mode), UNITS_PER_WORD)
|| !vect_worthwhile_without_simd_p (loop_vinfo, code))
return false;
if (dump_enabled_p ())
dump_printf (MSG_NOTE, "proceeding using word mode.\n");
}
/* Worthwhile without SIMD support? */
if (!VECTOR_MODE_P (TYPE_MODE (vectype_in))
&& !vect_worthwhile_without_simd_p (loop_vinfo, code))
{
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"not worthwhile without SIMD support.\n");
return false;
}
}
/* 4.2. Check support for the epilog operation.
If STMT represents a reduction pattern, then the type of the
reduction variable may be different than the type of the rest
of the arguments. For example, consider the case of accumulation
of shorts into an int accumulator; The original code:
S1: int_a = (int) short_a;
orig_stmt-> S2: int_acc = plus <int_a ,int_acc>;
was replaced with:
STMT: int_acc = widen_sum <short_a, int_acc>
This means that:
1. The tree-code that is used to create the vector operation in the
epilog code (that reduces the partial results) is not the
tree-code of STMT, but is rather the tree-code of the original
stmt from the pattern that STMT is replacing. I.e, in the example
above we want to use 'widen_sum' in the loop, but 'plus' in the
epilog.
2. The type (mode) we use to check available target support
for the vector operation to be created in the *epilog*, is
determined by the type of the reduction variable (in the example
above we'd check this: optab_handler (plus_optab, vect_int_mode])).
However the type (mode) we use to check available target support
for the vector operation to be created *inside the loop*, is
determined by the type of the other arguments to STMT (in the
example we'd check this: optab_handler (widen_sum_optab,
vect_short_mode)).
This is contrary to "regular" reductions, in which the types of all
the arguments are the same as the type of the reduction variable.
For "regular" reductions we can therefore use the same vector type
(and also the same tree-code) when generating the epilog code and
when generating the code inside the loop. */
vect_reduction_type reduction_type
= STMT_VINFO_VEC_REDUCTION_TYPE (stmt_info);
if (orig_stmt
&& (reduction_type == TREE_CODE_REDUCTION
|| reduction_type == FOLD_LEFT_REDUCTION))
{
/* This is a reduction pattern: get the vectype from the type of the
reduction variable, and get the tree-code from orig_stmt. */
orig_code = gimple_assign_rhs_code (orig_stmt);
gcc_assert (vectype_out);
vec_mode = TYPE_MODE (vectype_out);
}
else
{
/* Regular reduction: use the same vectype and tree-code as used for
the vector code inside the loop can be used for the epilog code. */
orig_code = code;
if (code == MINUS_EXPR)
orig_code = PLUS_EXPR;
/* For simple condition reductions, replace with the actual expression
we want to base our reduction around. */
if (reduction_type == CONST_COND_REDUCTION)
{
orig_code = STMT_VINFO_VEC_CONST_COND_REDUC_CODE (stmt_info);
gcc_assert (orig_code == MAX_EXPR || orig_code == MIN_EXPR);
}
else if (reduction_type == INTEGER_INDUC_COND_REDUCTION)
orig_code = cond_reduc_op_code;
}
if (nested_cycle)
{
def_bb = gimple_bb (reduc_def_stmt);
def_stmt_loop = def_bb->loop_father;
def_arg = PHI_ARG_DEF_FROM_EDGE (reduc_def_stmt,
loop_preheader_edge (def_stmt_loop));
if (TREE_CODE (def_arg) == SSA_NAME
&& (def_arg_stmt = SSA_NAME_DEF_STMT (def_arg))
&& gimple_code (def_arg_stmt) == GIMPLE_PHI
&& flow_bb_inside_loop_p (outer_loop, gimple_bb (def_arg_stmt))
&& vinfo_for_stmt (def_arg_stmt)
&& STMT_VINFO_DEF_TYPE (vinfo_for_stmt (def_arg_stmt))
== vect_double_reduction_def)
double_reduc = true;
}
reduc_fn = IFN_LAST;
if (reduction_type == TREE_CODE_REDUCTION
|| reduction_type == FOLD_LEFT_REDUCTION
|| reduction_type == INTEGER_INDUC_COND_REDUCTION
|| reduction_type == CONST_COND_REDUCTION)
{
if (reduction_type == FOLD_LEFT_REDUCTION
? fold_left_reduction_fn (orig_code, &reduc_fn)
: reduction_fn_for_scalar_code (orig_code, &reduc_fn))
{
if (reduc_fn != IFN_LAST
&& !direct_internal_fn_supported_p (reduc_fn, vectype_out,
OPTIMIZE_FOR_SPEED))
{
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"reduc op not supported by target.\n");
reduc_fn = IFN_LAST;
}
}
else
{
if (!nested_cycle || double_reduc)
{
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"no reduc code for scalar code.\n");
return false;
}
}
}
else if (reduction_type == COND_REDUCTION)
{
int scalar_precision
= GET_MODE_PRECISION (SCALAR_TYPE_MODE (scalar_type));
cr_index_scalar_type = make_unsigned_type (scalar_precision);
cr_index_vector_type = build_vector_type (cr_index_scalar_type,
nunits_out);
if (direct_internal_fn_supported_p (IFN_REDUC_MAX, cr_index_vector_type,
OPTIMIZE_FOR_SPEED))
reduc_fn = IFN_REDUC_MAX;
}
if (reduction_type != EXTRACT_LAST_REDUCTION
&& reduc_fn == IFN_LAST
&& !nunits_out.is_constant ())
{
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"missing target support for reduction on"
" variable-length vectors.\n");
return false;
}
if ((double_reduc || reduction_type != TREE_CODE_REDUCTION)
&& ncopies > 1)
{
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"multiple types in double reduction or condition "
"reduction.\n");
return false;
}
/* For SLP reductions, see if there is a neutral value we can use. */
tree neutral_op = NULL_TREE;
if (slp_node)
neutral_op
= neutral_op_for_slp_reduction (slp_node_instance->reduc_phis, code,
GROUP_FIRST_ELEMENT (stmt_info) != NULL);
if (double_reduc && reduction_type == FOLD_LEFT_REDUCTION)
{
/* We can't support in-order reductions of code such as this:
for (int i = 0; i < n1; ++i)
for (int j = 0; j < n2; ++j)
l += a[j];
since GCC effectively transforms the loop when vectorizing:
for (int i = 0; i < n1 / VF; ++i)
for (int j = 0; j < n2; ++j)
for (int k = 0; k < VF; ++k)
l += a[j];
which is a reassociation of the original operation. */
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"in-order double reduction not supported.\n");
return false;
}
if (reduction_type == FOLD_LEFT_REDUCTION
&& slp_node
&& !GROUP_FIRST_ELEMENT (vinfo_for_stmt (stmt)))
{
/* We cannot use in-order reductions in this case because there is
an implicit reassociation of the operations involved. */
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"in-order unchained SLP reductions not supported.\n");
return false;
}
/* For double reductions, and for SLP reductions with a neutral value,
we construct a variable-length initial vector by loading a vector
full of the neutral value and then shift-and-inserting the start
values into the low-numbered elements. */
if ((double_reduc || neutral_op)
&& !nunits_out.is_constant ()
&& !direct_internal_fn_supported_p (IFN_VEC_SHL_INSERT,
vectype_out, OPTIMIZE_FOR_SPEED))
{
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"reduction on variable-length vectors requires"
" target support for a vector-shift-and-insert"
" operation.\n");
return false;
}
/* Check extra constraints for variable-length unchained SLP reductions. */
if (STMT_SLP_TYPE (stmt_info)
&& !GROUP_FIRST_ELEMENT (vinfo_for_stmt (stmt))
&& !nunits_out.is_constant ())
{
/* We checked above that we could build the initial vector when
there's a neutral element value. Check here for the case in
which each SLP statement has its own initial value and in which
that value needs to be repeated for every instance of the
statement within the initial vector. */
unsigned int group_size = SLP_TREE_SCALAR_STMTS (slp_node).length ();
scalar_mode elt_mode = SCALAR_TYPE_MODE (TREE_TYPE (vectype_out));
if (!neutral_op
&& !can_duplicate_and_interleave_p (group_size, elt_mode))
{
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"unsupported form of SLP reduction for"
" variable-length vectors: cannot build"
" initial vector.\n");
return false;
}
/* The epilogue code relies on the number of elements being a multiple
of the group size. The duplicate-and-interleave approach to setting
up the the initial vector does too. */
if (!multiple_p (nunits_out, group_size))
{
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"unsupported form of SLP reduction for"
" variable-length vectors: the vector size"
" is not a multiple of the number of results.\n");
return false;
}
}
/* In case of widenning multiplication by a constant, we update the type
of the constant to be the type of the other operand. We check that the
constant fits the type in the pattern recognition pass. */
if (code == DOT_PROD_EXPR
&& !types_compatible_p (TREE_TYPE (ops[0]), TREE_TYPE (ops[1])))
{
if (TREE_CODE (ops[0]) == INTEGER_CST)
ops[0] = fold_convert (TREE_TYPE (ops[1]), ops[0]);
else if (TREE_CODE (ops[1]) == INTEGER_CST)
ops[1] = fold_convert (TREE_TYPE (ops[0]), ops[1]);
else
{
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"invalid types in dot-prod\n");
return false;
}
}
if (reduction_type == COND_REDUCTION)
{
widest_int ni;
if (! max_loop_iterations (loop, &ni))
{
if (dump_enabled_p ())
dump_printf_loc (MSG_NOTE, vect_location,
"loop count not known, cannot create cond "
"reduction.\n");
return false;
}
/* Convert backedges to iterations. */
ni += 1;
/* The additional index will be the same type as the condition. Check
that the loop can fit into this less one (because we'll use up the
zero slot for when there are no matches). */
tree max_index = TYPE_MAX_VALUE (cr_index_scalar_type);
if (wi::geu_p (ni, wi::to_widest (max_index)))
{
if (dump_enabled_p ())
dump_printf_loc (MSG_NOTE, vect_location,
"loop size is greater than data size.\n");
return false;
}
}
/* In case the vectorization factor (VF) is bigger than the number
of elements that we can fit in a vectype (nunits), we have to generate
more than one vector stmt - i.e - we need to "unroll" the
vector stmt by a factor VF/nunits. For more details see documentation
in vectorizable_operation. */
/* If the reduction is used in an outer loop we need to generate
VF intermediate results, like so (e.g. for ncopies=2):
r0 = phi (init, r0)
r1 = phi (init, r1)
r0 = x0 + r0;
r1 = x1 + r1;
(i.e. we generate VF results in 2 registers).
In this case we have a separate def-use cycle for each copy, and therefore
for each copy we get the vector def for the reduction variable from the
respective phi node created for this copy.
Otherwise (the reduction is unused in the loop nest), we can combine
together intermediate results, like so (e.g. for ncopies=2):
r = phi (init, r)
r = x0 + r;
r = x1 + r;
(i.e. we generate VF/2 results in a single register).
In this case for each copy we get the vector def for the reduction variable
from the vectorized reduction operation generated in the previous iteration.
This only works when we see both the reduction PHI and its only consumer
in vectorizable_reduction and there are no intermediate stmts
participating. */
use_operand_p use_p;
gimple *use_stmt;
if (ncopies > 1
&& (STMT_VINFO_RELEVANT (stmt_info) <= vect_used_only_live)
&& single_imm_use (gimple_phi_result (reduc_def_stmt), &use_p, &use_stmt)
&& (use_stmt == stmt
|| STMT_VINFO_RELATED_STMT (vinfo_for_stmt (use_stmt)) == stmt))
{
single_defuse_cycle = true;
epilog_copies = 1;
}
else
epilog_copies = ncopies;
/* If the reduction stmt is one of the patterns that have lane
reduction embedded we cannot handle the case of ! single_defuse_cycle. */
if ((ncopies > 1
&& ! single_defuse_cycle)
&& (code == DOT_PROD_EXPR
|| code == WIDEN_SUM_EXPR
|| code == SAD_EXPR))
{
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"multi def-use cycle not possible for lane-reducing "
"reduction operation\n");
return false;
}
if (slp_node)
vec_num = SLP_TREE_NUMBER_OF_VEC_STMTS (slp_node);
else
vec_num = 1;
internal_fn cond_fn = get_conditional_internal_fn (code);
vec_loop_masks *masks = &LOOP_VINFO_MASKS (loop_vinfo);
if (!vec_stmt) /* transformation not required. */
{
if (first_p)
vect_model_reduction_cost (stmt_info, reduc_fn, ncopies);
if (loop_vinfo && LOOP_VINFO_CAN_FULLY_MASK_P (loop_vinfo))
{
if (reduction_type != FOLD_LEFT_REDUCTION
&& (cond_fn == IFN_LAST
|| !direct_internal_fn_supported_p (cond_fn, vectype_in,
OPTIMIZE_FOR_SPEED)))
{
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"can't use a fully-masked loop because no"
" conditional operation is available.\n");
LOOP_VINFO_CAN_FULLY_MASK_P (loop_vinfo) = false;
}
else if (reduc_index == -1)
{
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"can't use a fully-masked loop for chained"
" reductions.\n");
LOOP_VINFO_CAN_FULLY_MASK_P (loop_vinfo) = false;
}
else
vect_record_loop_mask (loop_vinfo, masks, ncopies * vec_num,
vectype_in);
}
if (dump_enabled_p ()
&& reduction_type == FOLD_LEFT_REDUCTION)
dump_printf_loc (MSG_NOTE, vect_location,
"using an in-order (fold-left) reduction.\n");
STMT_VINFO_TYPE (stmt_info) = reduc_vec_info_type;
return true;
}
/* Transform. */
if (dump_enabled_p ())
dump_printf_loc (MSG_NOTE, vect_location, "transform reduction.\n");
/* FORNOW: Multiple types are not supported for condition. */
if (code == COND_EXPR)
gcc_assert (ncopies == 1);
bool masked_loop_p = LOOP_VINFO_FULLY_MASKED_P (loop_vinfo);
if (reduction_type == FOLD_LEFT_REDUCTION)
return vectorize_fold_left_reduction
(stmt, gsi, vec_stmt, slp_node, reduc_def_stmt, code,
reduc_fn, ops, vectype_in, reduc_index, masks);
if (reduction_type == EXTRACT_LAST_REDUCTION)
{
gcc_assert (!slp_node);
return vectorizable_condition (stmt, gsi, vec_stmt,
NULL, reduc_index, NULL);
}
/* Create the destination vector */
vec_dest = vect_create_destination_var (scalar_dest, vectype_out);
prev_stmt_info = NULL;
prev_phi_info = NULL;
if (!slp_node)
{
vec_oprnds0.create (1);
vec_oprnds1.create (1);
if (op_type == ternary_op)
vec_oprnds2.create (1);
}
phis.create (vec_num);
vect_defs.create (vec_num);
if (!slp_node)
vect_defs.quick_push (NULL_TREE);
if (slp_node)
phis.splice (SLP_TREE_VEC_STMTS (slp_node_instance->reduc_phis));
else
phis.quick_push (STMT_VINFO_VEC_STMT (vinfo_for_stmt (reduc_def_stmt)));
for (j = 0; j < ncopies; j++)
{
if (code == COND_EXPR)
{
gcc_assert (!slp_node);
vectorizable_condition (stmt, gsi, vec_stmt,
PHI_RESULT (phis[0]),
reduc_index, NULL);
/* Multiple types are not supported for condition. */
break;
}
/* Handle uses. */
if (j == 0)
{
if (slp_node)
{
/* Get vec defs for all the operands except the reduction index,
ensuring the ordering of the ops in the vector is kept. */
auto_vec<tree, 3> slp_ops;
auto_vec<vec<tree>, 3> vec_defs;
slp_ops.quick_push (ops[0]);
slp_ops.quick_push (ops[1]);
if (op_type == ternary_op)
slp_ops.quick_push (ops[2]);
vect_get_slp_defs (slp_ops, slp_node, &vec_defs);
vec_oprnds0.safe_splice (vec_defs[0]);
vec_defs[0].release ();
vec_oprnds1.safe_splice (vec_defs[1]);
vec_defs[1].release ();
if (op_type == ternary_op)
{
vec_oprnds2.safe_splice (vec_defs[2]);
vec_defs[2].release ();
}
}
else
{
vec_oprnds0.quick_push
(vect_get_vec_def_for_operand (ops[0], stmt));
vec_oprnds1.quick_push
(vect_get_vec_def_for_operand (ops[1], stmt));
if (op_type == ternary_op)
vec_oprnds2.quick_push
(vect_get_vec_def_for_operand (ops[2], stmt));
}
}
else
{
if (!slp_node)
{
gcc_assert (reduc_index != -1 || ! single_defuse_cycle);
if (single_defuse_cycle && reduc_index == 0)
vec_oprnds0[0] = gimple_get_lhs (new_stmt);
else
vec_oprnds0[0]
= vect_get_vec_def_for_stmt_copy (dts[0], vec_oprnds0[0]);
if (single_defuse_cycle && reduc_index == 1)
vec_oprnds1[0] = gimple_get_lhs (new_stmt);
else
vec_oprnds1[0]
= vect_get_vec_def_for_stmt_copy (dts[1], vec_oprnds1[0]);
if (op_type == ternary_op)
{
if (single_defuse_cycle && reduc_index == 2)
vec_oprnds2[0] = gimple_get_lhs (new_stmt);
else
vec_oprnds2[0]
= vect_get_vec_def_for_stmt_copy (dts[2], vec_oprnds2[0]);
}
}
}
FOR_EACH_VEC_ELT (vec_oprnds0, i, def0)
{
tree vop[3] = { def0, vec_oprnds1[i], NULL_TREE };
if (masked_loop_p)
{
/* Make sure that the reduction accumulator is vop[0]. */
if (reduc_index == 1)
{
gcc_assert (commutative_tree_code (code));
std::swap (vop[0], vop[1]);
}
tree mask = vect_get_loop_mask (gsi, masks, vec_num * ncopies,
vectype_in, i * ncopies + j);
gcall *call = gimple_build_call_internal (cond_fn, 3, mask,
vop[0], vop[1]);
new_temp = make_ssa_name (vec_dest, call);
gimple_call_set_lhs (call, new_temp);
gimple_call_set_nothrow (call, true);
new_stmt = call;
}
else
{
if (op_type == ternary_op)
vop[2] = vec_oprnds2[i];
new_temp = make_ssa_name (vec_dest, new_stmt);
new_stmt = gimple_build_assign (new_temp, code,
vop[0], vop[1], vop[2]);
}
vect_finish_stmt_generation (stmt, new_stmt, gsi);
if (slp_node)
{
SLP_TREE_VEC_STMTS (slp_node).quick_push (new_stmt);
vect_defs.quick_push (new_temp);
}
else
vect_defs[0] = new_temp;
}
if (slp_node)
continue;
if (j == 0)
STMT_VINFO_VEC_STMT (stmt_info) = *vec_stmt = new_stmt;
else
STMT_VINFO_RELATED_STMT (prev_stmt_info) = new_stmt;
prev_stmt_info = vinfo_for_stmt (new_stmt);
}
/* Finalize the reduction-phi (set its arguments) and create the
epilog reduction code. */
if ((!single_defuse_cycle || code == COND_EXPR) && !slp_node)
vect_defs[0] = gimple_get_lhs (*vec_stmt);
vect_create_epilog_for_reduction (vect_defs, stmt, reduc_def_stmt,
epilog_copies, reduc_fn, phis,
double_reduc, slp_node, slp_node_instance,
cond_reduc_val, cond_reduc_op_code,
neutral_op);
return true;
}
/* Function vect_min_worthwhile_factor.
For a loop where we could vectorize the operation indicated by CODE,
return the minimum vectorization factor that makes it worthwhile
to use generic vectors. */
static unsigned int
vect_min_worthwhile_factor (enum tree_code code)
{
switch (code)
{
case PLUS_EXPR:
case MINUS_EXPR:
case NEGATE_EXPR:
return 4;
case BIT_AND_EXPR:
case BIT_IOR_EXPR:
case BIT_XOR_EXPR:
case BIT_NOT_EXPR:
return 2;
default:
return INT_MAX;
}
}
/* Return true if VINFO indicates we are doing loop vectorization and if
it is worth decomposing CODE operations into scalar operations for
that loop's vectorization factor. */
bool
vect_worthwhile_without_simd_p (vec_info *vinfo, tree_code code)
{
loop_vec_info loop_vinfo = dyn_cast <loop_vec_info> (vinfo);
unsigned HOST_WIDE_INT value;
return (loop_vinfo
&& LOOP_VINFO_VECT_FACTOR (loop_vinfo).is_constant (&value)
&& value >= vect_min_worthwhile_factor (code));
}
/* Function vectorizable_induction
Check if PHI performs an induction computation that can be vectorized.
If VEC_STMT is also passed, vectorize the induction PHI: create a vectorized
phi to replace it, put it in VEC_STMT, and add it to the same basic block.
Return FALSE if not a vectorizable STMT, TRUE otherwise. */
bool
vectorizable_induction (gimple *phi,
gimple_stmt_iterator *gsi ATTRIBUTE_UNUSED,
gimple **vec_stmt, slp_tree slp_node)
{
stmt_vec_info stmt_info = vinfo_for_stmt (phi);
loop_vec_info loop_vinfo = STMT_VINFO_LOOP_VINFO (stmt_info);
struct loop *loop = LOOP_VINFO_LOOP (loop_vinfo);
unsigned ncopies;
bool nested_in_vect_loop = false;
struct loop *iv_loop;
tree vec_def;
edge pe = loop_preheader_edge (loop);
basic_block new_bb;
tree new_vec, vec_init, vec_step, t;
tree new_name;
gimple *new_stmt;
gphi *induction_phi;
tree induc_def, vec_dest;
tree init_expr, step_expr;
poly_uint64 vf = LOOP_VINFO_VECT_FACTOR (loop_vinfo);
unsigned i;
tree expr;
gimple_seq stmts;
imm_use_iterator imm_iter;
use_operand_p use_p;
gimple *exit_phi;
edge latch_e;
tree loop_arg;
gimple_stmt_iterator si;
basic_block bb = gimple_bb (phi);
if (gimple_code (phi) != GIMPLE_PHI)
return false;
if (!STMT_VINFO_RELEVANT_P (stmt_info))
return false;
/* Make sure it was recognized as induction computation. */
if (STMT_VINFO_DEF_TYPE (stmt_info) != vect_induction_def)
return false;
tree vectype = STMT_VINFO_VECTYPE (stmt_info);
poly_uint64 nunits = TYPE_VECTOR_SUBPARTS (vectype);
if (slp_node)
ncopies = 1;
else
ncopies = vect_get_num_copies (loop_vinfo, vectype);
gcc_assert (ncopies >= 1);
/* FORNOW. These restrictions should be relaxed. */
if (nested_in_vect_loop_p (loop, phi))
{
imm_use_iterator imm_iter;
use_operand_p use_p;
gimple *exit_phi;
edge latch_e;
tree loop_arg;
if (ncopies > 1)
{
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"multiple types in nested loop.\n");
return false;
}
/* FORNOW: outer loop induction with SLP not supported. */
if (STMT_SLP_TYPE (stmt_info))
return false;
exit_phi = NULL;
latch_e = loop_latch_edge (loop->inner);
loop_arg = PHI_ARG_DEF_FROM_EDGE (phi, latch_e);
FOR_EACH_IMM_USE_FAST (use_p, imm_iter, loop_arg)
{
gimple *use_stmt = USE_STMT (use_p);
if (is_gimple_debug (use_stmt))
continue;
if (!flow_bb_inside_loop_p (loop->inner, gimple_bb (use_stmt)))
{
exit_phi = use_stmt;
break;
}
}
if (exit_phi)
{
stmt_vec_info exit_phi_vinfo = vinfo_for_stmt (exit_phi);
if (!(STMT_VINFO_RELEVANT_P (exit_phi_vinfo)
&& !STMT_VINFO_LIVE_P (exit_phi_vinfo)))
{
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"inner-loop induction only used outside "
"of the outer vectorized loop.\n");
return false;
}
}
nested_in_vect_loop = true;
iv_loop = loop->inner;
}
else
iv_loop = loop;
gcc_assert (iv_loop == (gimple_bb (phi))->loop_father);
if (slp_node && !nunits.is_constant ())
{
/* The current SLP code creates the initial value element-by-element. */
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"SLP induction not supported for variable-length"
" vectors.\n");
return false;
}
if (!vec_stmt) /* transformation not required. */
{
STMT_VINFO_TYPE (stmt_info) = induc_vec_info_type;
if (dump_enabled_p ())
dump_printf_loc (MSG_NOTE, vect_location,
"=== vectorizable_induction ===\n");
vect_model_induction_cost (stmt_info, ncopies);
return true;
}
/* Transform. */
/* Compute a vector variable, initialized with the first VF values of
the induction variable. E.g., for an iv with IV_PHI='X' and
evolution S, for a vector of 4 units, we want to compute:
[X, X + S, X + 2*S, X + 3*S]. */
if (dump_enabled_p ())
dump_printf_loc (MSG_NOTE, vect_location, "transform induction phi.\n");
latch_e = loop_latch_edge (iv_loop);
loop_arg = PHI_ARG_DEF_FROM_EDGE (phi, latch_e);
step_expr = STMT_VINFO_LOOP_PHI_EVOLUTION_PART (stmt_info);
gcc_assert (step_expr != NULL_TREE);
pe = loop_preheader_edge (iv_loop);
init_expr = PHI_ARG_DEF_FROM_EDGE (phi,
loop_preheader_edge (iv_loop));
stmts = NULL;
if (!nested_in_vect_loop)
{
/* Convert the initial value to the desired type. */
tree new_type = TREE_TYPE (vectype);
init_expr = gimple_convert (&stmts, new_type, init_expr);
/* If we are using the loop mask to "peel" for alignment then we need
to adjust the start value here. */
tree skip_niters = LOOP_VINFO_MASK_SKIP_NITERS (loop_vinfo);
if (skip_niters != NULL_TREE)
{
if (FLOAT_TYPE_P (vectype))
skip_niters = gimple_build (&stmts, FLOAT_EXPR, new_type,
skip_niters);
else
skip_niters = gimple_convert (&stmts, new_type, skip_niters);
tree skip_step = gimple_build (&stmts, MULT_EXPR, new_type,
skip_niters, step_expr);
init_expr = gimple_build (&stmts, MINUS_EXPR, new_type,
init_expr, skip_step);
}
}
/* Convert the step to the desired type. */
step_expr = gimple_convert (&stmts, TREE_TYPE (vectype), step_expr);
if (stmts)
{
new_bb = gsi_insert_seq_on_edge_immediate (pe, stmts);
gcc_assert (!new_bb);
}
/* Find the first insertion point in the BB. */
si = gsi_after_labels (bb);
/* For SLP induction we have to generate several IVs as for example
with group size 3 we need [i, i, i, i + S] [i + S, i + S, i + 2*S, i + 2*S]
[i + 2*S, i + 3*S, i + 3*S, i + 3*S]. The step is the same uniform
[VF*S, VF*S, VF*S, VF*S] for all. */
if (slp_node)
{
/* Enforced above. */
unsigned int const_nunits = nunits.to_constant ();
/* Generate [VF*S, VF*S, ... ]. */
if (SCALAR_FLOAT_TYPE_P (TREE_TYPE (step_expr)))
{
expr = build_int_cst (integer_type_node, vf);
expr = fold_convert (TREE_TYPE (step_expr), expr);
}
else
expr = build_int_cst (TREE_TYPE (step_expr), vf);
new_name = fold_build2 (MULT_EXPR, TREE_TYPE (step_expr),
expr, step_expr);
if (! CONSTANT_CLASS_P (new_name))
new_name = vect_init_vector (phi, new_name,
TREE_TYPE (step_expr), NULL);
new_vec = build_vector_from_val (vectype, new_name);
vec_step = vect_init_vector (phi, new_vec, vectype, NULL);
/* Now generate the IVs. */
unsigned group_size = SLP_TREE_SCALAR_STMTS (slp_node).length ();
unsigned nvects = SLP_TREE_NUMBER_OF_VEC_STMTS (slp_node);
unsigned elts = const_nunits * nvects;
unsigned nivs = least_common_multiple (group_size,
const_nunits) / const_nunits;
gcc_assert (elts % group_size == 0);
tree elt = init_expr;
unsigned ivn;
for (ivn = 0; ivn < nivs; ++ivn)
{
tree_vector_builder elts (vectype, const_nunits, 1);
stmts = NULL;
for (unsigned eltn = 0; eltn < const_nunits; ++eltn)
{
if (ivn*const_nunits + eltn >= group_size
&& (ivn * const_nunits + eltn) % group_size == 0)
elt = gimple_build (&stmts, PLUS_EXPR, TREE_TYPE (elt),
elt, step_expr);
elts.quick_push (elt);
}
vec_init = gimple_build_vector (&stmts, &elts);
if (stmts)
{
new_bb = gsi_insert_seq_on_edge_immediate (pe, stmts);
gcc_assert (!new_bb);
}
/* Create the induction-phi that defines the induction-operand. */
vec_dest = vect_get_new_vect_var (vectype, vect_simple_var, "vec_iv_");
induction_phi = create_phi_node (vec_dest, iv_loop->header);
set_vinfo_for_stmt (induction_phi,
new_stmt_vec_info (induction_phi, loop_vinfo));
induc_def = PHI_RESULT (induction_phi);
/* Create the iv update inside the loop */
vec_def = make_ssa_name (vec_dest);
new_stmt = gimple_build_assign (vec_def, PLUS_EXPR, induc_def, vec_step);
gsi_insert_before (&si, new_stmt, GSI_SAME_STMT);
set_vinfo_for_stmt (new_stmt, new_stmt_vec_info (new_stmt, loop_vinfo));
/* Set the arguments of the phi node: */
add_phi_arg (induction_phi, vec_init, pe, UNKNOWN_LOCATION);
add_phi_arg (induction_phi, vec_def, loop_latch_edge (iv_loop),
UNKNOWN_LOCATION);
SLP_TREE_VEC_STMTS (slp_node).quick_push (induction_phi);
}
/* Re-use IVs when we can. */
if (ivn < nvects)
{
unsigned vfp
= least_common_multiple (group_size, const_nunits) / group_size;
/* Generate [VF'*S, VF'*S, ... ]. */
if (SCALAR_FLOAT_TYPE_P (TREE_TYPE (step_expr)))
{
expr = build_int_cst (integer_type_node, vfp);
expr = fold_convert (TREE_TYPE (step_expr), expr);
}
else
expr = build_int_cst (TREE_TYPE (step_expr), vfp);
new_name = fold_build2 (MULT_EXPR, TREE_TYPE (step_expr),
expr, step_expr);
if (! CONSTANT_CLASS_P (new_name))
new_name = vect_init_vector (phi, new_name,
TREE_TYPE (step_expr), NULL);
new_vec = build_vector_from_val (vectype, new_name);
vec_step = vect_init_vector (phi, new_vec, vectype, NULL);
for (; ivn < nvects; ++ivn)
{
gimple *iv = SLP_TREE_VEC_STMTS (slp_node)[ivn - nivs];
tree def;
if (gimple_code (iv) == GIMPLE_PHI)
def = gimple_phi_result (iv);
else
def = gimple_assign_lhs (iv);
new_stmt = gimple_build_assign (make_ssa_name (vectype),
PLUS_EXPR,
def, vec_step);
if (gimple_code (iv) == GIMPLE_PHI)
gsi_insert_before (&si, new_stmt, GSI_SAME_STMT);
else
{
gimple_stmt_iterator tgsi = gsi_for_stmt (iv);
gsi_insert_after (&tgsi, new_stmt, GSI_CONTINUE_LINKING);
}
set_vinfo_for_stmt (new_stmt,
new_stmt_vec_info (new_stmt, loop_vinfo));
SLP_TREE_VEC_STMTS (slp_node).quick_push (new_stmt);
}
}
return true;
}
/* Create the vector that holds the initial_value of the induction. */
if (nested_in_vect_loop)
{
/* iv_loop is nested in the loop to be vectorized. init_expr had already
been created during vectorization of previous stmts. We obtain it
from the STMT_VINFO_VEC_STMT of the defining stmt. */
vec_init = vect_get_vec_def_for_operand (init_expr, phi);
/* If the initial value is not of proper type, convert it. */
if (!useless_type_conversion_p (vectype, TREE_TYPE (vec_init)))
{
new_stmt
= gimple_build_assign (vect_get_new_ssa_name (vectype,
vect_simple_var,
"vec_iv_"),
VIEW_CONVERT_EXPR,
build1 (VIEW_CONVERT_EXPR, vectype,
vec_init));
vec_init = gimple_assign_lhs (new_stmt);
new_bb = gsi_insert_on_edge_immediate (loop_preheader_edge (iv_loop),
new_stmt);
gcc_assert (!new_bb);
set_vinfo_for_stmt (new_stmt,
new_stmt_vec_info (new_stmt, loop_vinfo));
}
}
else
{
/* iv_loop is the loop to be vectorized. Create:
vec_init = [X, X+S, X+2*S, X+3*S] (S = step_expr, X = init_expr) */
stmts = NULL;
new_name = gimple_convert (&stmts, TREE_TYPE (vectype), init_expr);
unsigned HOST_WIDE_INT const_nunits;
if (nunits.is_constant (&const_nunits))
{
tree_vector_builder elts (vectype, const_nunits, 1);
elts.quick_push (new_name);
for (i = 1; i < const_nunits; i++)
{
/* Create: new_name_i = new_name + step_expr */
new_name = gimple_build (&stmts, PLUS_EXPR, TREE_TYPE (new_name),
new_name, step_expr);
elts.quick_push (new_name);
}
/* Create a vector from [new_name_0, new_name_1, ...,
new_name_nunits-1] */
vec_init = gimple_build_vector (&stmts, &elts);
}
else if (INTEGRAL_TYPE_P (TREE_TYPE (step_expr)))
/* Build the initial value directly from a VEC_SERIES_EXPR. */
vec_init = gimple_build (&stmts, VEC_SERIES_EXPR, vectype,
new_name, step_expr);
else
{
/* Build:
[base, base, base, ...]
+ (vectype) [0, 1, 2, ...] * [step, step, step, ...]. */
gcc_assert (SCALAR_FLOAT_TYPE_P (TREE_TYPE (step_expr)));
gcc_assert (flag_associative_math);
tree index = build_index_vector (vectype, 0, 1);
tree base_vec = gimple_build_vector_from_val (&stmts, vectype,
new_name);
tree step_vec = gimple_build_vector_from_val (&stmts, vectype,
step_expr);
vec_init = gimple_build (&stmts, FLOAT_EXPR, vectype, index);
vec_init = gimple_build (&stmts, MULT_EXPR, vectype,
vec_init, step_vec);
vec_init = gimple_build (&stmts, PLUS_EXPR, vectype,
vec_init, base_vec);
}
if (stmts)
{
new_bb = gsi_insert_seq_on_edge_immediate (pe, stmts);
gcc_assert (!new_bb);
}
}
/* Create the vector that holds the step of the induction. */
if (nested_in_vect_loop)
/* iv_loop is nested in the loop to be vectorized. Generate:
vec_step = [S, S, S, S] */
new_name = step_expr;
else
{
/* iv_loop is the loop to be vectorized. Generate:
vec_step = [VF*S, VF*S, VF*S, VF*S] */
gimple_seq seq = NULL;
if (SCALAR_FLOAT_TYPE_P (TREE_TYPE (step_expr)))
{
expr = build_int_cst (integer_type_node, vf);
expr = gimple_build (&seq, FLOAT_EXPR, TREE_TYPE (step_expr), expr);
}
else
expr = build_int_cst (TREE_TYPE (step_expr), vf);
new_name = gimple_build (&seq, MULT_EXPR, TREE_TYPE (step_expr),
expr, step_expr);
if (seq)
{
new_bb = gsi_insert_seq_on_edge_immediate (pe, seq);
gcc_assert (!new_bb);
}
}
t = unshare_expr (new_name);
gcc_assert (CONSTANT_CLASS_P (new_name)
|| TREE_CODE (new_name) == SSA_NAME);
new_vec = build_vector_from_val (vectype, t);
vec_step = vect_init_vector (phi, new_vec, vectype, NULL);
/* Create the following def-use cycle:
loop prolog:
vec_init = ...
vec_step = ...
loop:
vec_iv = PHI <vec_init, vec_loop>
...
STMT
...
vec_loop = vec_iv + vec_step; */
/* Create the induction-phi that defines the induction-operand. */
vec_dest = vect_get_new_vect_var (vectype, vect_simple_var, "vec_iv_");
induction_phi = create_phi_node (vec_dest, iv_loop->header);
set_vinfo_for_stmt (induction_phi,
new_stmt_vec_info (induction_phi, loop_vinfo));
induc_def = PHI_RESULT (induction_phi);
/* Create the iv update inside the loop */
vec_def = make_ssa_name (vec_dest);
new_stmt = gimple_build_assign (vec_def, PLUS_EXPR, induc_def, vec_step);
gsi_insert_before (&si, new_stmt, GSI_SAME_STMT);
set_vinfo_for_stmt (new_stmt, new_stmt_vec_info (new_stmt, loop_vinfo));
/* Set the arguments of the phi node: */
add_phi_arg (induction_phi, vec_init, pe, UNKNOWN_LOCATION);
add_phi_arg (induction_phi, vec_def, loop_latch_edge (iv_loop),
UNKNOWN_LOCATION);
STMT_VINFO_VEC_STMT (stmt_info) = *vec_stmt = induction_phi;
/* In case that vectorization factor (VF) is bigger than the number
of elements that we can fit in a vectype (nunits), we have to generate
more than one vector stmt - i.e - we need to "unroll" the
vector stmt by a factor VF/nunits. For more details see documentation
in vectorizable_operation. */
if (ncopies > 1)
{
gimple_seq seq = NULL;
stmt_vec_info prev_stmt_vinfo;
/* FORNOW. This restriction should be relaxed. */
gcc_assert (!nested_in_vect_loop);
/* Create the vector that holds the step of the induction. */
if (SCALAR_FLOAT_TYPE_P (TREE_TYPE (step_expr)))
{
expr = build_int_cst (integer_type_node, nunits);
expr = gimple_build (&seq, FLOAT_EXPR, TREE_TYPE (step_expr), expr);
}
else
expr = build_int_cst (TREE_TYPE (step_expr), nunits);
new_name = gimple_build (&seq, MULT_EXPR, TREE_TYPE (step_expr),
expr, step_expr);
if (seq)
{
new_bb = gsi_insert_seq_on_edge_immediate (pe, seq);
gcc_assert (!new_bb);
}
t = unshare_expr (new_name);
gcc_assert (CONSTANT_CLASS_P (new_name)
|| TREE_CODE (new_name) == SSA_NAME);
new_vec = build_vector_from_val (vectype, t);
vec_step = vect_init_vector (phi, new_vec, vectype, NULL);
vec_def = induc_def;
prev_stmt_vinfo = vinfo_for_stmt (induction_phi);
for (i = 1; i < ncopies; i++)
{
/* vec_i = vec_prev + vec_step */
new_stmt = gimple_build_assign (vec_dest, PLUS_EXPR,
vec_def, vec_step);
vec_def = make_ssa_name (vec_dest, new_stmt);
gimple_assign_set_lhs (new_stmt, vec_def);
gsi_insert_before (&si, new_stmt, GSI_SAME_STMT);
set_vinfo_for_stmt (new_stmt,
new_stmt_vec_info (new_stmt, loop_vinfo));
STMT_VINFO_RELATED_STMT (prev_stmt_vinfo) = new_stmt;
prev_stmt_vinfo = vinfo_for_stmt (new_stmt);
}
}
if (nested_in_vect_loop)
{
/* Find the loop-closed exit-phi of the induction, and record
the final vector of induction results: */
exit_phi = NULL;
FOR_EACH_IMM_USE_FAST (use_p, imm_iter, loop_arg)
{
gimple *use_stmt = USE_STMT (use_p);
if (is_gimple_debug (use_stmt))
continue;
if (!flow_bb_inside_loop_p (iv_loop, gimple_bb (use_stmt)))
{
exit_phi = use_stmt;
break;
}
}
if (exit_phi)
{
stmt_vec_info stmt_vinfo = vinfo_for_stmt (exit_phi);
/* FORNOW. Currently not supporting the case that an inner-loop induction
is not used in the outer-loop (i.e. only outside the outer-loop). */
gcc_assert (STMT_VINFO_RELEVANT_P (stmt_vinfo)
&& !STMT_VINFO_LIVE_P (stmt_vinfo));
STMT_VINFO_VEC_STMT (stmt_vinfo) = new_stmt;
if (dump_enabled_p ())
{
dump_printf_loc (MSG_NOTE, vect_location,
"vector of inductions after inner-loop:");
dump_gimple_stmt (MSG_NOTE, TDF_SLIM, new_stmt, 0);
}
}
}
if (dump_enabled_p ())
{
dump_printf_loc (MSG_NOTE, vect_location,
"transform induction: created def-use cycle: ");
dump_gimple_stmt (MSG_NOTE, TDF_SLIM, induction_phi, 0);
dump_gimple_stmt (MSG_NOTE, TDF_SLIM,
SSA_NAME_DEF_STMT (vec_def), 0);
}
return true;
}
/* Function vectorizable_live_operation.
STMT computes a value that is used outside the loop. Check if
it can be supported. */
bool
vectorizable_live_operation (gimple *stmt,
gimple_stmt_iterator *gsi ATTRIBUTE_UNUSED,
slp_tree slp_node, int slp_index,
gimple **vec_stmt)
{
stmt_vec_info stmt_info = vinfo_for_stmt (stmt);
loop_vec_info loop_vinfo = STMT_VINFO_LOOP_VINFO (stmt_info);
struct loop *loop = LOOP_VINFO_LOOP (loop_vinfo);
imm_use_iterator imm_iter;
tree lhs, lhs_type, bitsize, vec_bitsize;
tree vectype = STMT_VINFO_VECTYPE (stmt_info);
poly_uint64 nunits = TYPE_VECTOR_SUBPARTS (vectype);
int ncopies;
gimple *use_stmt;
auto_vec<tree> vec_oprnds;
int vec_entry = 0;
poly_uint64 vec_index = 0;
gcc_assert (STMT_VINFO_LIVE_P (stmt_info));
if (STMT_VINFO_DEF_TYPE (stmt_info) == vect_reduction_def)
return false;
/* FORNOW. CHECKME. */
if (nested_in_vect_loop_p (loop, stmt))
return false;
/* If STMT is not relevant and it is a simple assignment and its inputs are
invariant then it can remain in place, unvectorized. The original last
scalar value that it computes will be used. */
if (!STMT_VINFO_RELEVANT_P (stmt_info))
{
gcc_assert (is_simple_and_all_uses_invariant (stmt, loop_vinfo));
if (dump_enabled_p ())
dump_printf_loc (MSG_NOTE, vect_location,
"statement is simple and uses invariant. Leaving in "
"place.\n");
return true;
}
if (slp_node)
ncopies = 1;
else
ncopies = vect_get_num_copies (loop_vinfo, vectype);
if (slp_node)
{
gcc_assert (slp_index >= 0);
int num_scalar = SLP_TREE_SCALAR_STMTS (slp_node).length ();
int num_vec = SLP_TREE_NUMBER_OF_VEC_STMTS (slp_node);
/* Get the last occurrence of the scalar index from the concatenation of
all the slp vectors. Calculate which slp vector it is and the index
within. */
poly_uint64 pos = (num_vec * nunits) - num_scalar + slp_index;
/* Calculate which vector contains the result, and which lane of
that vector we need. */
if (!can_div_trunc_p (pos, nunits, &vec_entry, &vec_index))
{
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"Cannot determine which vector holds the"
" final result.\n");
return false;
}
}
if (!vec_stmt)
{
/* No transformation required. */
if (LOOP_VINFO_CAN_FULLY_MASK_P (loop_vinfo))
{
if (!direct_internal_fn_supported_p (IFN_EXTRACT_LAST, vectype,
OPTIMIZE_FOR_SPEED))
{
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"can't use a fully-masked loop because "
"the target doesn't support extract last "
"reduction.\n");
LOOP_VINFO_CAN_FULLY_MASK_P (loop_vinfo) = false;
}
else if (slp_node)
{
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"can't use a fully-masked loop because an "
"SLP statement is live after the loop.\n");
LOOP_VINFO_CAN_FULLY_MASK_P (loop_vinfo) = false;
}
else if (ncopies > 1)
{
if (dump_enabled_p ())
dump_printf_loc (MSG_MISSED_OPTIMIZATION, vect_location,
"can't use a fully-masked loop because"
" ncopies is greater than 1.\n");
LOOP_VINFO_CAN_FULLY_MASK_P (loop_vinfo) = false;
}
else
{
gcc_assert (ncopies == 1 && !slp_node);
vect_record_loop_mask (loop_vinfo,
&LOOP_VINFO_MASKS (loop_vinfo),
1, vectype);
}
}
return true;
}
/* If stmt has a related stmt, then use that for getting the lhs. */
if (is_pattern_stmt_p (stmt_info))
stmt = STMT_VINFO_RELATED_STMT (stmt_info);
lhs = (is_a <gphi *> (stmt)) ? gimple_phi_result (stmt)
: gimple_get_lhs (stmt);
lhs_type = TREE_TYPE (lhs);
bitsize = (VECTOR_BOOLEAN_TYPE_P (vectype)
? bitsize_int (TYPE_PRECISION (TREE_TYPE (vectype)))
: TYPE_SIZE (TREE_TYPE (vectype)));
vec_bitsize = TYPE_SIZE (vectype);
/* Get the vectorized lhs of STMT and the lane to use (counted in bits). */
tree vec_lhs, bitstart;
if (slp_node)
{
gcc_assert (!LOOP_VINFO_FULLY_MASKED_P (loop_vinfo));
/* Get the correct slp vectorized stmt. */
gimple *vec_stmt = SLP_TREE_VEC_STMTS (slp_node)[vec_entry];
if (gphi *phi = dyn_cast <gphi *> (vec_stmt))
vec_lhs = gimple_phi_result (phi);
else
vec_lhs = gimple_get_lhs (vec_stmt);
/* Get entry to use. */
bitstart = bitsize_int (vec_index);
bitstart = int_const_binop (MULT_EXPR, bitsize, bitstart);
}
else
{
enum vect_def_type dt = STMT_VINFO_DEF_TYPE (stmt_info);
vec_lhs = vect_get_vec_def_for_operand_1 (stmt, dt);
gcc_checking_assert (ncopies == 1
|| !LOOP_VINFO_FULLY_MASKED_P (loop_vinfo));
/* For multiple copies, get the last copy. */
for (int i = 1; i < ncopies; ++i)
vec_lhs = vect_get_vec_def_for_stmt_copy (vect_unknown_def_type,
vec_lhs);
/* Get the last lane in the vector. */
bitstart = int_const_binop (MINUS_EXPR, vec_bitsize, bitsize);
}
gimple_seq stmts = NULL;
tree new_tree;
if (LOOP_VINFO_FULLY_MASKED_P (loop_vinfo))
{
/* Emit:
SCALAR_RES = EXTRACT_LAST <VEC_LHS, MASK>
where VEC_LHS is the vectorized live-out result and MASK is
the loop mask for the final iteration. */
gcc_assert (ncopies == 1 && !slp_node);
tree scalar_type = TREE_TYPE (STMT_VINFO_VECTYPE (stmt_info));
tree scalar_res = make_ssa_name (scalar_type);
tree mask = vect_get_loop_mask (gsi, &LOOP_VINFO_MASKS (loop_vinfo),
1, vectype, 0);
gcall *new_stmt = gimple_build_call_internal (IFN_EXTRACT_LAST,
2, mask, vec_lhs);
gimple_call_set_lhs (new_stmt, scalar_res);
gimple_seq_add_stmt (&stmts, new_stmt);
/* Convert the extracted vector element to the required scalar type. */
new_tree = gimple_convert (&stmts, lhs_type, scalar_res);
}
else
{
tree bftype = TREE_TYPE (vectype);
if (VECTOR_BOOLEAN_TYPE_P (vectype))
bftype = build_nonstandard_integer_type (tree_to_uhwi (bitsize), 1);
new_tree = build3 (BIT_FIELD_REF, bftype, vec_lhs, bitsize, bitstart);
new_tree = force_gimple_operand (fold_convert (lhs_type, new_tree),
&stmts, true, NULL_TREE);
}
if (stmts)
gsi_insert_seq_on_edge_immediate (single_exit (loop), stmts);
/* Replace use of lhs with newly computed result. If the use stmt is a
single arg PHI, just replace all uses of PHI result. It's necessary
because lcssa PHI defining lhs may be before newly inserted stmt. */
use_operand_p use_p;
FOR_EACH_IMM_USE_STMT (use_stmt, imm_iter, lhs)
if (!flow_bb_inside_loop_p (loop, gimple_bb (use_stmt))
&& !is_gimple_debug (use_stmt))
{
if (gimple_code (use_stmt) == GIMPLE_PHI
&& gimple_phi_num_args (use_stmt) == 1)
{
replace_uses_by (gimple_phi_result (use_stmt), new_tree);
}
else
{
FOR_EACH_IMM_USE_ON_STMT (use_p, imm_iter)
SET_USE (use_p, new_tree);
}
update_stmt (use_stmt);
}
return true;
}
/* Kill any debug uses outside LOOP of SSA names defined in STMT. */
static void
vect_loop_kill_debug_uses (struct loop *loop, gimple *stmt)
{
ssa_op_iter op_iter;
imm_use_iterator imm_iter;
def_operand_p def_p;
gimple *ustmt;
FOR_EACH_PHI_OR_STMT_DEF (def_p, stmt, op_iter, SSA_OP_DEF)
{
FOR_EACH_IMM_USE_STMT (ustmt, imm_iter, DEF_FROM_PTR (def_p))
{
basic_block bb;
if (!is_gimple_debug (ustmt))
continue;
bb = gimple_bb (ustmt);
if (!flow_bb_inside_loop_p (loop, bb))
{
if (gimple_debug_bind_p (ustmt))
{
if (dump_enabled_p ())
dump_printf_loc (MSG_NOTE, vect_location,
"killing debug use\n");
gimple_debug_bind_reset_value (ustmt);
update_stmt (ustmt);
}
else
gcc_unreachable ();
}
}
}
}
/* Given loop represented by LOOP_VINFO, return true if computation of
LOOP_VINFO_NITERS (= LOOP_VINFO_NITERSM1 + 1) doesn't overflow, false
otherwise. */
static bool
loop_niters_no_overflow (loop_vec_info loop_vinfo)
{
/* Constant case. */
if (LOOP_VINFO_NITERS_KNOWN_P (loop_vinfo))
{
tree cst_niters = LOOP_VINFO_NITERS (loop_vinfo);
tree cst_nitersm1 = LOOP_VINFO_NITERSM1 (loop_vinfo);
gcc_assert (TREE_CODE (cst_niters) == INTEGER_CST);
gcc_assert (TREE_CODE (cst_nitersm1) == INTEGER_CST);
if (wi::to_widest (cst_nitersm1) < wi::to_widest (cst_niters))
return true;
}
widest_int max;
struct loop *loop = LOOP_VINFO_LOOP (loop_vinfo);
/* Check the upper bound of loop niters. */
if (get_max_loop_iterations (loop, &max))
{
tree type = TREE_TYPE (LOOP_VINFO_NITERS (loop_vinfo));
signop sgn = TYPE_SIGN (type);
widest_int type_max = widest_int::from (wi::max_value (type), sgn);
if (max < type_max)
return true;
}
return false;
}
/* Return a mask type with half the number of elements as TYPE. */
tree
vect_halve_mask_nunits (tree type)
{
poly_uint64 nunits = exact_div (TYPE_VECTOR_SUBPARTS (type), 2);
return build_truth_vector_type (nunits, current_vector_size);
}
/* Return a mask type with twice as many elements as TYPE. */
tree
vect_double_mask_nunits (tree type)
{
poly_uint64 nunits = TYPE_VECTOR_SUBPARTS (type) * 2;
return build_truth_vector_type (nunits, current_vector_size);
}
/* Record that a fully-masked version of LOOP_VINFO would need MASKS to
contain a sequence of NVECTORS masks that each control a vector of type
VECTYPE. */
void
vect_record_loop_mask (loop_vec_info loop_vinfo, vec_loop_masks *masks,
unsigned int nvectors, tree vectype)
{
gcc_assert (nvectors != 0);
if (masks->length () < nvectors)
masks->safe_grow_cleared (nvectors);
rgroup_masks *rgm = &(*masks)[nvectors - 1];
/* The number of scalars per iteration and the number of vectors are
both compile-time constants. */
unsigned int nscalars_per_iter
= exact_div (nvectors * TYPE_VECTOR_SUBPARTS (vectype),
LOOP_VINFO_VECT_FACTOR (loop_vinfo)).to_constant ();
if (rgm->max_nscalars_per_iter < nscalars_per_iter)
{
rgm->max_nscalars_per_iter = nscalars_per_iter;
rgm->mask_type = build_same_sized_truth_vector_type (vectype);
}
}
/* Given a complete set of masks MASKS, extract mask number INDEX
for an rgroup that operates on NVECTORS vectors of type VECTYPE,
where 0 <= INDEX < NVECTORS. Insert any set-up statements before GSI.
See the comment above vec_loop_masks for more details about the mask
arrangement. */
tree
vect_get_loop_mask (gimple_stmt_iterator *gsi, vec_loop_masks *masks,
unsigned int nvectors, tree vectype, unsigned int index)
{
rgroup_masks *rgm = &(*masks)[nvectors - 1];
tree mask_type = rgm->mask_type;
/* Populate the rgroup's mask array, if this is the first time we've
used it. */
if (rgm->masks.is_empty ())
{
rgm->masks.safe_grow_cleared (nvectors);
for (unsigned int i = 0; i < nvectors; ++i)
{
tree mask = make_temp_ssa_name (mask_type, NULL, "loop_mask");
/* Provide a dummy definition until the real one is available. */
SSA_NAME_DEF_STMT (mask) = gimple_build_nop ();
rgm->masks[i] = mask;
}
}
tree mask = rgm->masks[index];
if (maybe_ne (TYPE_VECTOR_SUBPARTS (mask_type),
TYPE_VECTOR_SUBPARTS (vectype)))
{
/* A loop mask for data type X can be reused for data type Y
if X has N times more elements than Y and if Y's elements
are N times bigger than X's. In this case each sequence
of N elements in the loop mask will be all-zero or all-one.
We can then view-convert the mask so that each sequence of
N elements is replaced by a single element. */
gcc_assert (multiple_p (TYPE_VECTOR_SUBPARTS (mask_type),
TYPE_VECTOR_SUBPARTS (vectype)));
gimple_seq seq = NULL;
mask_type = build_same_sized_truth_vector_type (vectype);
mask = gimple_build (&seq, VIEW_CONVERT_EXPR, mask_type, mask);
if (seq)
gsi_insert_seq_before (gsi, seq, GSI_SAME_STMT);
}
return mask;
}
/* Scale profiling counters by estimation for LOOP which is vectorized
by factor VF. */
static void
scale_profile_for_vect_loop (struct loop *loop, unsigned vf)
{
edge preheader = loop_preheader_edge (loop);
/* Reduce loop iterations by the vectorization factor. */
gcov_type new_est_niter = niter_for_unrolled_loop (loop, vf);
profile_count freq_h = loop->header->count, freq_e = preheader->count ();
if (freq_h.nonzero_p ())
{
profile_probability p;
/* Avoid dropping loop body profile counter to 0 because of zero count
in loop's preheader. */
if (!(freq_e == profile_count::zero ()))
freq_e = freq_e.force_nonzero ();
p = freq_e.apply_scale (new_est_niter + 1, 1).probability_in (freq_h);
scale_loop_frequencies (loop, p);
}
edge exit_e = single_exit (loop);
exit_e->probability = profile_probability::always ()
.apply_scale (1, new_est_niter + 1);
edge exit_l = single_pred_edge (loop->latch);
profile_probability prob = exit_l->probability;
exit_l->probability = exit_e->probability.invert ();
if (prob.initialized_p () && exit_l->probability.initialized_p ())
scale_bbs_frequencies (&loop->latch, 1, exit_l->probability / prob);
}
/* Function vect_transform_loop.
The analysis phase has determined that the loop is vectorizable.
Vectorize the loop - created vectorized stmts to replace the scalar
stmts in the loop, and update the loop exit condition.
Returns scalar epilogue loop if any. */
struct loop *
vect_transform_loop (loop_vec_info loop_vinfo)
{
struct loop *loop = LOOP_VINFO_LOOP (loop_vinfo);
struct loop *epilogue = NULL;
basic_block *bbs = LOOP_VINFO_BBS (loop_vinfo);
int nbbs = loop->num_nodes;
int i;
tree niters_vector = NULL_TREE;
tree step_vector = NULL_TREE;
tree niters_vector_mult_vf = NULL_TREE;
poly_uint64 vf = LOOP_VINFO_VECT_FACTOR (loop_vinfo);
unsigned int lowest_vf = constant_lower_bound (vf);
bool grouped_store;
bool slp_scheduled = false;
gimple *stmt, *pattern_stmt;
gimple_seq pattern_def_seq = NULL;
gimple_stmt_iterator pattern_def_si = gsi_none ();
bool transform_pattern_stmt = false;
bool check_profitability = false;
unsigned int th;
if (dump_enabled_p ())
dump_printf_loc (MSG_NOTE, vect_location, "=== vec_transform_loop ===\n");
/* Use the more conservative vectorization threshold. If the number
of iterations is constant assume the cost check has been performed
by our caller. If the threshold makes all loops profitable that
run at least the (estimated) vectorization factor number of times
checking is pointless, too. */
th = LOOP_VINFO_COST_MODEL_THRESHOLD (loop_vinfo);
if (th >= vect_vf_for_cost (loop_vinfo)
&& !LOOP_VINFO_NITERS_KNOWN_P (loop_vinfo))
{
if (dump_enabled_p ())
dump_printf_loc (MSG_NOTE, vect_location,
"Profitability threshold is %d loop iterations.\n",
th);
check_profitability = true;
}
/* Make sure there exists a single-predecessor exit bb. Do this before
versioning. */
edge e = single_exit (loop);
if (! single_pred_p (e->dest))
{
split_loop_exit_edge (e);
if (dump_enabled_p ())
dump_printf (MSG_NOTE, "split exit edge\n");
}
/* Version the loop first, if required, so the profitability check
comes first. */
if (LOOP_REQUIRES_VERSIONING (loop_vinfo))
{
poly_uint64 versioning_threshold
= LOOP_VINFO_VERSIONING_THRESHOLD (loop_vinfo);
if (check_profitability
&& ordered_p (poly_uint64 (th), versioning_threshold))
{
versioning_threshold = ordered_max (poly_uint64 (th),
versioning_threshold);
check_profitability = false;
}
vect_loop_versioning (loop_vinfo, th, check_profitability,
versioning_threshold);
check_profitability = false;
}
/* Make sure there exists a single-predecessor exit bb also on the
scalar loop copy. Do this after versioning but before peeling
so CFG structure is fine for both scalar and if-converted loop
to make slpeel_duplicate_current_defs_from_edges face matched
loop closed PHI nodes on the exit. */
if (LOOP_VINFO_SCALAR_LOOP (loop_vinfo))
{
e = single_exit (LOOP_VINFO_SCALAR_LOOP (loop_vinfo));
if (! single_pred_p (e->dest))
{
split_loop_exit_edge (e);
if (dump_enabled_p ())
dump_printf (MSG_NOTE, "split exit edge of scalar loop\n");
}
}
tree niters = vect_build_loop_niters (loop_vinfo);
LOOP_VINFO_NITERS_UNCHANGED (loop_vinfo) = niters;
tree nitersm1 = unshare_expr (LOOP_VINFO_NITERSM1 (loop_vinfo));
bool niters_no_overflow = loop_niters_no_overflow (loop_vinfo);
epilogue = vect_do_peeling (loop_vinfo, niters, nitersm1, &niters_vector,
&step_vector, &niters_vector_mult_vf, th,
check_profitability, niters_no_overflow);
if (niters_vector == NULL_TREE)
{
if (LOOP_VINFO_NITERS_KNOWN_P (loop_vinfo)
&& !LOOP_VINFO_FULLY_MASKED_P (loop_vinfo)
&& known_eq (lowest_vf, vf))
{
niters_vector
= build_int_cst (TREE_TYPE (LOOP_VINFO_NITERS (loop_vinfo)),
LOOP_VINFO_INT_NITERS (loop_vinfo) / lowest_vf);
step_vector = build_one_cst (TREE_TYPE (niters));
}
else
vect_gen_vector_loop_niters (loop_vinfo, niters, &niters_vector,
&step_vector, niters_no_overflow);
}
/* 1) Make sure the loop header has exactly two entries
2) Make sure we have a preheader basic block. */
gcc_assert (EDGE_COUNT (loop->header->preds) == 2);
split_edge (loop_preheader_edge (loop));
if (LOOP_VINFO_FULLY_MASKED_P (loop_vinfo)
&& vect_use_loop_mask_for_alignment_p (loop_vinfo))
/* This will deal with any possible peeling. */
vect_prepare_for_masked_peels (loop_vinfo);
/* FORNOW: the vectorizer supports only loops which body consist
of one basic block (header + empty latch). When the vectorizer will
support more involved loop forms, the order by which the BBs are
traversed need to be reconsidered. */
for (i = 0; i < nbbs; i++)
{
basic_block bb = bbs[i];
stmt_vec_info stmt_info;
for (gphi_iterator si = gsi_start_phis (bb); !gsi_end_p (si);
gsi_next (&si))
{
gphi *phi = si.phi ();
if (dump_enabled_p ())
{
dump_printf_loc (MSG_NOTE, vect_location,
"------>vectorizing phi: ");
dump_gimple_stmt (MSG_NOTE, TDF_SLIM, phi, 0);
}
stmt_info = vinfo_for_stmt (phi);
if (!stmt_info)
continue;
if (MAY_HAVE_DEBUG_BIND_STMTS && !STMT_VINFO_LIVE_P (stmt_info))
vect_loop_kill_debug_uses (loop, phi);
if (!STMT_VINFO_RELEVANT_P (stmt_info)
&& !STMT_VINFO_LIVE_P (stmt_info))
continue;
if (STMT_VINFO_VECTYPE (stmt_info)
&& (maybe_ne
(TYPE_VECTOR_SUBPARTS (STMT_VINFO_VECTYPE (stmt_info)), vf))
&& dump_enabled_p ())
dump_printf_loc (MSG_NOTE, vect_location, "multiple-types.\n");
if ((STMT_VINFO_DEF_TYPE (stmt_info) == vect_induction_def
|| STMT_VINFO_DEF_TYPE (stmt_info) == vect_reduction_def
|| STMT_VINFO_DEF_TYPE (stmt_info) == vect_nested_cycle)
&& ! PURE_SLP_STMT (stmt_info))
{
if (dump_enabled_p ())
dump_printf_loc (MSG_NOTE, vect_location, "transform phi.\n");
vect_transform_stmt (phi, NULL, NULL, NULL, NULL);
}
}
pattern_stmt = NULL;
for (gimple_stmt_iterator si = gsi_start_bb (bb);
!gsi_end_p (si) || transform_pattern_stmt;)
{
bool is_store;
if (transform_pattern_stmt)
stmt = pattern_stmt;
else
{
stmt = gsi_stmt (si);
/* During vectorization remove existing clobber stmts. */
if (gimple_clobber_p (stmt))
{
unlink_stmt_vdef (stmt);
gsi_remove (&si, true);
release_defs (stmt);
continue;
}
}
if (dump_enabled_p ())
{
dump_printf_loc (MSG_NOTE, vect_location,
"------>vectorizing statement: ");
dump_gimple_stmt (MSG_NOTE, TDF_SLIM, stmt, 0);
}
stmt_info = vinfo_for_stmt (stmt);
/* vector stmts created in the outer-loop during vectorization of
stmts in an inner-loop may not have a stmt_info, and do not
need to be vectorized. */
if (!stmt_info)
{
gsi_next (&si);
continue;
}
if (MAY_HAVE_DEBUG_BIND_STMTS && !STMT_VINFO_LIVE_P (stmt_info))
vect_loop_kill_debug_uses (loop, stmt);
if (!STMT_VINFO_RELEVANT_P (stmt_info)
&& !STMT_VINFO_LIVE_P (stmt_info))
{
if (STMT_VINFO_IN_PATTERN_P (stmt_info)
&& (pattern_stmt = STMT_VINFO_RELATED_STMT (stmt_info))
&& (STMT_VINFO_RELEVANT_P (vinfo_for_stmt (pattern_stmt))
|| STMT_VINFO_LIVE_P (vinfo_for_stmt (pattern_stmt))))
{
stmt = pattern_stmt;
stmt_info = vinfo_for_stmt (stmt);
}
else
{
gsi_next (&si);
continue;
}
}
else if (STMT_VINFO_IN_PATTERN_P (stmt_info)
&& (pattern_stmt = STMT_VINFO_RELATED_STMT (stmt_info))
&& (STMT_VINFO_RELEVANT_P (vinfo_for_stmt (pattern_stmt))
|| STMT_VINFO_LIVE_P (vinfo_for_stmt (pattern_stmt))))
transform_pattern_stmt = true;
/* If pattern statement has def stmts, vectorize them too. */
if (is_pattern_stmt_p (stmt_info))
{
if (pattern_def_seq == NULL)
{
pattern_def_seq = STMT_VINFO_PATTERN_DEF_SEQ (stmt_info);
pattern_def_si = gsi_start (pattern_def_seq);
}
else if (!gsi_end_p (pattern_def_si))
gsi_next (&pattern_def_si);
if (pattern_def_seq != NULL)
{
gimple *pattern_def_stmt = NULL;
stmt_vec_info pattern_def_stmt_info = NULL;
while (!gsi_end_p (pattern_def_si))
{
pattern_def_stmt = gsi_stmt (pattern_def_si);
pattern_def_stmt_info
= vinfo_for_stmt (pattern_def_stmt);
if (STMT_VINFO_RELEVANT_P (pattern_def_stmt_info)
|| STMT_VINFO_LIVE_P (pattern_def_stmt_info))
break;
gsi_next (&pattern_def_si);
}
if (!gsi_end_p (pattern_def_si))
{
if (dump_enabled_p ())
{
dump_printf_loc (MSG_NOTE, vect_location,
"==> vectorizing pattern def "
"stmt: ");
dump_gimple_stmt (MSG_NOTE, TDF_SLIM,
pattern_def_stmt, 0);
}
stmt = pattern_def_stmt;
stmt_info = pattern_def_stmt_info;
}
else
{
pattern_def_si = gsi_none ();
transform_pattern_stmt = false;
}
}
else
transform_pattern_stmt = false;
}
if (STMT_VINFO_VECTYPE (stmt_info))
{
poly_uint64 nunits
= TYPE_VECTOR_SUBPARTS (STMT_VINFO_VECTYPE (stmt_info));
if (!STMT_SLP_TYPE (stmt_info)
&& maybe_ne (nunits, vf)
&& dump_enabled_p ())
/* For SLP VF is set according to unrolling factor, and not
to vector size, hence for SLP this print is not valid. */
dump_printf_loc (MSG_NOTE, vect_location, "multiple-types.\n");
}
/* SLP. Schedule all the SLP instances when the first SLP stmt is
reached. */
if (STMT_SLP_TYPE (stmt_info))
{
if (!slp_scheduled)
{
slp_scheduled = true;
if (dump_enabled_p ())
dump_printf_loc (MSG_NOTE, vect_location,
"=== scheduling SLP instances ===\n");
vect_schedule_slp (loop_vinfo);
}
/* Hybrid SLP stmts must be vectorized in addition to SLP. */
if (!vinfo_for_stmt (stmt) || PURE_SLP_STMT (stmt_info))
{
if (!transform_pattern_stmt && gsi_end_p (pattern_def_si))
{
pattern_def_seq = NULL;
gsi_next (&si);
}
continue;
}
}
/* -------- vectorize statement ------------ */
if (dump_enabled_p ())
dump_printf_loc (MSG_NOTE, vect_location, "transform statement.\n");
grouped_store = false;
is_store = vect_transform_stmt (stmt, &si, &grouped_store, NULL, NULL);
if (is_store)
{
if (STMT_VINFO_GROUPED_ACCESS (stmt_info))
{
/* Interleaving. If IS_STORE is TRUE, the vectorization of the
interleaving chain was completed - free all the stores in
the chain. */
gsi_next (&si);
vect_remove_stores (GROUP_FIRST_ELEMENT (stmt_info));
}
else
{
/* Free the attached stmt_vec_info and remove the stmt. */
gimple *store = gsi_stmt (si);
free_stmt_vec_info (store);
unlink_stmt_vdef (store);
gsi_remove (&si, true);
release_defs (store);
}
/* Stores can only appear at the end of pattern statements. */
gcc_assert (!transform_pattern_stmt);
pattern_def_seq = NULL;
}
else if (!transform_pattern_stmt && gsi_end_p (pattern_def_si))
{
pattern_def_seq = NULL;
gsi_next (&si);
}
} /* stmts in BB */
/* Stub out scalar statements that must not survive vectorization.
Doing this here helps with grouped statements, or statements that
are involved in patterns. */
for (gimple_stmt_iterator gsi = gsi_start_bb (bb);
!gsi_end_p (gsi); gsi_next (&gsi))
{
gcall *call = dyn_cast <gcall *> (gsi_stmt (gsi));
if (call && gimple_call_internal_p (call, IFN_MASK_LOAD))
{
tree lhs = gimple_get_lhs (call);
if (!VECTOR_TYPE_P (TREE_TYPE (lhs)))
{
tree zero = build_zero_cst (TREE_TYPE (lhs));
gimple *new_stmt = gimple_build_assign (lhs, zero);
gsi_replace (&gsi, new_stmt, true);
}
}
}
} /* BBs in loop */
/* The vectorization factor is always > 1, so if we use an IV increment of 1.
a zero NITERS becomes a nonzero NITERS_VECTOR. */
if (integer_onep (step_vector))
niters_no_overflow = true;
vect_set_loop_condition (loop, loop_vinfo, niters_vector, step_vector,
niters_vector_mult_vf, !niters_no_overflow);
unsigned int assumed_vf = vect_vf_for_cost (loop_vinfo);
scale_profile_for_vect_loop (loop, assumed_vf);
/* True if the final iteration might not handle a full vector's
worth of scalar iterations. */
bool final_iter_may_be_partial = LOOP_VINFO_FULLY_MASKED_P (loop_vinfo);
/* The minimum number of iterations performed by the epilogue. This
is 1 when peeling for gaps because we always need a final scalar
iteration. */
int min_epilogue_iters = LOOP_VINFO_PEELING_FOR_GAPS (loop_vinfo) ? 1 : 0;
/* +1 to convert latch counts to loop iteration counts,
-min_epilogue_iters to remove iterations that cannot be performed
by the vector code. */
int bias_for_lowest = 1 - min_epilogue_iters;
int bias_for_assumed = bias_for_lowest;
int alignment_npeels = LOOP_VINFO_PEELING_FOR_ALIGNMENT (loop_vinfo);
if (alignment_npeels && LOOP_VINFO_FULLY_MASKED_P (loop_vinfo))
{
/* When the amount of peeling is known at compile time, the first
iteration will have exactly alignment_npeels active elements.
In the worst case it will have at least one. */
int min_first_active = (alignment_npeels > 0 ? alignment_npeels : 1);
bias_for_lowest += lowest_vf - min_first_active;
bias_for_assumed += assumed_vf - min_first_active;
}
/* In these calculations the "- 1" converts loop iteration counts
back to latch counts. */
if (loop->any_upper_bound)
loop->nb_iterations_upper_bound
= (final_iter_may_be_partial
? wi::udiv_ceil (loop->nb_iterations_upper_bound + bias_for_lowest,
lowest_vf) - 1
: wi::udiv_floor (loop->nb_iterations_upper_bound + bias_for_lowest,
lowest_vf) - 1);
if (loop->any_likely_upper_bound)
loop->nb_iterations_likely_upper_bound
= (final_iter_may_be_partial
? wi::udiv_ceil (loop->nb_iterations_likely_upper_bound
+ bias_for_lowest, lowest_vf) - 1
: wi::udiv_floor (loop->nb_iterations_likely_upper_bound
+ bias_for_lowest, lowest_vf) - 1);
if (loop->any_estimate)
loop->nb_iterations_estimate
= (final_iter_may_be_partial
? wi::udiv_ceil (loop->nb_iterations_estimate + bias_for_assumed,
assumed_vf) - 1
: wi::udiv_floor (loop->nb_iterations_estimate + bias_for_assumed,
assumed_vf) - 1);
if (dump_enabled_p ())
{
if (!LOOP_VINFO_EPILOGUE_P (loop_vinfo))
{
dump_printf_loc (MSG_NOTE, vect_location,
"LOOP VECTORIZED\n");
if (loop->inner)
dump_printf_loc (MSG_NOTE, vect_location,
"OUTER LOOP VECTORIZED\n");
dump_printf (MSG_NOTE, "\n");
}
else
{
dump_printf_loc (MSG_NOTE, vect_location,
"LOOP EPILOGUE VECTORIZED (VS=");
dump_dec (MSG_NOTE, current_vector_size);
dump_printf (MSG_NOTE, ")\n");
}
}
/* Free SLP instances here because otherwise stmt reference counting
won't work. */
slp_instance instance;
FOR_EACH_VEC_ELT (LOOP_VINFO_SLP_INSTANCES (loop_vinfo), i, instance)
vect_free_slp_instance (instance);
LOOP_VINFO_SLP_INSTANCES (loop_vinfo).release ();
/* Clear-up safelen field since its value is invalid after vectorization
since vectorized loop can have loop-carried dependencies. */
loop->safelen = 0;
/* Don't vectorize epilogue for epilogue. */
if (LOOP_VINFO_EPILOGUE_P (loop_vinfo))
epilogue = NULL;
if (!PARAM_VALUE (PARAM_VECT_EPILOGUES_NOMASK))
epilogue = NULL;
if (epilogue)
{
auto_vector_sizes vector_sizes;
targetm.vectorize.autovectorize_vector_sizes (&vector_sizes);
unsigned int next_size = 0;
if (LOOP_VINFO_NITERS_KNOWN_P (loop_vinfo)
&& LOOP_VINFO_PEELING_FOR_ALIGNMENT (loop_vinfo) >= 0
&& known_eq (vf, lowest_vf))
{
unsigned int eiters
= (LOOP_VINFO_INT_NITERS (loop_vinfo)
- LOOP_VINFO_PEELING_FOR_ALIGNMENT (loop_vinfo));
eiters = eiters % lowest_vf;
epilogue->nb_iterations_upper_bound = eiters - 1;
unsigned int ratio;
while (next_size < vector_sizes.length ()
&& !(constant_multiple_p (current_vector_size,
vector_sizes[next_size], &ratio)
&& eiters >= lowest_vf / ratio))
next_size += 1;
}
else
while (next_size < vector_sizes.length ()
&& maybe_lt (current_vector_size, vector_sizes[next_size]))
next_size += 1;
if (next_size == vector_sizes.length ())
epilogue = NULL;
}
if (epilogue)
{
epilogue->force_vectorize = loop->force_vectorize;
epilogue->safelen = loop->safelen;
epilogue->dont_vectorize = false;
/* We may need to if-convert epilogue to vectorize it. */
if (LOOP_VINFO_SCALAR_LOOP (loop_vinfo))
tree_if_conversion (epilogue);
}
return epilogue;
}
/* The code below is trying to perform simple optimization - revert
if-conversion for masked stores, i.e. if the mask of a store is zero
do not perform it and all stored value producers also if possible.
For example,
for (i=0; i<n; i++)
if (c[i])
{
p1[i] += 1;
p2[i] = p3[i] +2;
}
this transformation will produce the following semi-hammock:
if (!mask__ifc__42.18_165 == { 0, 0, 0, 0, 0, 0, 0, 0 })
{
vect__11.19_170 = MASK_LOAD (vectp_p1.20_168, 0B, mask__ifc__42.18_165);
vect__12.22_172 = vect__11.19_170 + vect_cst__171;
MASK_STORE (vectp_p1.23_175, 0B, mask__ifc__42.18_165, vect__12.22_172);
vect__18.25_182 = MASK_LOAD (vectp_p3.26_180, 0B, mask__ifc__42.18_165);
vect__19.28_184 = vect__18.25_182 + vect_cst__183;
MASK_STORE (vectp_p2.29_187, 0B, mask__ifc__42.18_165, vect__19.28_184);
}
*/
void
optimize_mask_stores (struct loop *loop)
{
basic_block *bbs = get_loop_body (loop);
unsigned nbbs = loop->num_nodes;
unsigned i;
basic_block bb;
struct loop *bb_loop;
gimple_stmt_iterator gsi;
gimple *stmt;
auto_vec<gimple *> worklist;
vect_location = find_loop_location (loop);
/* Pick up all masked stores in loop if any. */
for (i = 0; i < nbbs; i++)
{
bb = bbs[i];
for (gsi = gsi_start_bb (bb); !gsi_end_p (gsi);
gsi_next (&gsi))
{
stmt = gsi_stmt (gsi);
if (gimple_call_internal_p (stmt, IFN_MASK_STORE))
worklist.safe_push (stmt);
}
}
free (bbs);
if (worklist.is_empty ())
return;
/* Loop has masked stores. */
while (!worklist.is_empty ())
{
gimple *last, *last_store;
edge e, efalse;
tree mask;
basic_block store_bb, join_bb;
gimple_stmt_iterator gsi_to;
tree vdef, new_vdef;
gphi *phi;
tree vectype;
tree zero;
last = worklist.pop ();
mask = gimple_call_arg (last, 2);
bb = gimple_bb (last);
/* Create then_bb and if-then structure in CFG, then_bb belongs to
the same loop as if_bb. It could be different to LOOP when two
level loop-nest is vectorized and mask_store belongs to the inner
one. */
e = split_block (bb, last);
bb_loop = bb->loop_father;
gcc_assert (loop == bb_loop || flow_loop_nested_p (loop, bb_loop));
join_bb = e->dest;
store_bb = create_empty_bb (bb);
add_bb_to_loop (store_bb, bb_loop);
e->flags = EDGE_TRUE_VALUE;
efalse = make_edge (bb, store_bb, EDGE_FALSE_VALUE);
/* Put STORE_BB to likely part. */
efalse->probability = profile_probability::unlikely ();
store_bb->count = efalse->count ();
make_single_succ_edge (store_bb, join_bb, EDGE_FALLTHRU);
if (dom_info_available_p (CDI_DOMINATORS))
set_immediate_dominator (CDI_DOMINATORS, store_bb, bb);
if (dump_enabled_p ())
dump_printf_loc (MSG_NOTE, vect_location,
"Create new block %d to sink mask stores.",
store_bb->index);
/* Create vector comparison with boolean result. */
vectype = TREE_TYPE (mask);
zero = build_zero_cst (vectype);
stmt = gimple_build_cond (EQ_EXPR, mask, zero, NULL_TREE, NULL_TREE);
gsi = gsi_last_bb (bb);
gsi_insert_after (&gsi, stmt, GSI_SAME_STMT);
/* Create new PHI node for vdef of the last masked store:
.MEM_2 = VDEF <.MEM_1>
will be converted to
.MEM.3 = VDEF <.MEM_1>
and new PHI node will be created in join bb
.MEM_2 = PHI <.MEM_1, .MEM_3>
*/
vdef = gimple_vdef (last);
new_vdef = make_ssa_name (gimple_vop (cfun), last);
gimple_set_vdef (last, new_vdef);
phi = create_phi_node (vdef, join_bb);
add_phi_arg (phi, new_vdef, EDGE_SUCC (store_bb, 0), UNKNOWN_LOCATION);
/* Put all masked stores with the same mask to STORE_BB if possible. */
while (true)
{
gimple_stmt_iterator gsi_from;
gimple *stmt1 = NULL;
/* Move masked store to STORE_BB. */
last_store = last;
gsi = gsi_for_stmt (last);
gsi_from = gsi;
/* Shift GSI to the previous stmt for further traversal. */
gsi_prev (&gsi);
gsi_to = gsi_start_bb (store_bb);
gsi_move_before (&gsi_from, &gsi_to);
/* Setup GSI_TO to the non-empty block start. */
gsi_to = gsi_start_bb (store_bb);
if (dump_enabled_p ())
{
dump_printf_loc (MSG_NOTE, vect_location,
"Move stmt to created bb\n");
dump_gimple_stmt (MSG_NOTE, TDF_SLIM, last, 0);
}
/* Move all stored value producers if possible. */
while (!gsi_end_p (gsi))
{
tree lhs;
imm_use_iterator imm_iter;
use_operand_p use_p;
bool res;
/* Skip debug statements. */
if (is_gimple_debug (gsi_stmt (gsi)))
{
gsi_prev (&gsi);
continue;
}
stmt1 = gsi_stmt (gsi);
/* Do not consider statements writing to memory or having
volatile operand. */
if (gimple_vdef (stmt1)
|| gimple_has_volatile_ops (stmt1))
break;
gsi_from = gsi;
gsi_prev (&gsi);
lhs = gimple_get_lhs (stmt1);
if (!lhs)
break;
/* LHS of vectorized stmt must be SSA_NAME. */
if (TREE_CODE (lhs) != SSA_NAME)
break;
if (!VECTOR_TYPE_P (TREE_TYPE (lhs)))
{
/* Remove dead scalar statement. */
if (has_zero_uses (lhs))
{
gsi_remove (&gsi_from, true);
continue;
}
}
/* Check that LHS does not have uses outside of STORE_BB. */
res = true;
FOR_EACH_IMM_USE_FAST (use_p, imm_iter, lhs)
{
gimple *use_stmt;
use_stmt = USE_STMT (use_p);
if (is_gimple_debug (use_stmt))
continue;
if (gimple_bb (use_stmt) != store_bb)
{
res = false;
break;
}
}
if (!res)
break;
if (gimple_vuse (stmt1)
&& gimple_vuse (stmt1) != gimple_vuse (last_store))
break;
/* Can move STMT1 to STORE_BB. */
if (dump_enabled_p ())
{
dump_printf_loc (MSG_NOTE, vect_location,
"Move stmt to created bb\n");
dump_gimple_stmt (MSG_NOTE, TDF_SLIM, stmt1, 0);
}
gsi_move_before (&gsi_from, &gsi_to);
/* Shift GSI_TO for further insertion. */
gsi_prev (&gsi_to);
}
/* Put other masked stores with the same mask to STORE_BB. */
if (worklist.is_empty ()
|| gimple_call_arg (worklist.last (), 2) != mask
|| worklist.last () != stmt1)
break;
last = worklist.pop ();
}
add_phi_arg (phi, gimple_vuse (last_store), e, UNKNOWN_LOCATION);
}
}