@c Copyright (C) 2004-2021 Free Software Foundation, Inc.
@c This is part of the GCC manual.
@c For copying conditions, see the file gcc.texi.

@c ---------------------------------------------------------------------
@c Tree SSA
@c ---------------------------------------------------------------------

@node Tree SSA
@chapter Analysis and Optimization of GIMPLE tuples
@cindex Tree SSA
@cindex Optimization infrastructure for GIMPLE

GCC uses three main intermediate languages to represent the program
during compilation: GENERIC, GIMPLE and RTL@.  GENERIC is a
language-independent representation generated by each front end.  It
is used to serve as an interface between the parser and optimizer.
GENERIC is a common representation that is able to represent programs
written in all the languages supported by GCC@.

GIMPLE and RTL are used to optimize the program.  GIMPLE is used for
target and language independent optimizations (e.g., inlining,
constant propagation, tail call elimination, redundancy elimination,
etc).  Much like GENERIC, GIMPLE is a language independent, tree based
representation.  However, it differs from GENERIC in that the GIMPLE
grammar is more restrictive: expressions contain no more than 3
operands (except function calls), it has no control flow structures
and expressions with side effects are only allowed on the right hand
side of assignments.  See the chapter describing GENERIC and GIMPLE
for more details.

This chapter describes the data structures and functions used in the
GIMPLE optimizers (also known as ``tree optimizers'' or ``middle
end'').  In particular, it focuses on all the macros, data structures,
functions and programming constructs needed to implement optimization
passes for GIMPLE@.

@menu
* Annotations::         Attributes for variables.
* SSA Operands::        SSA names referenced by GIMPLE statements.
* SSA::                 Static Single Assignment representation.
* Alias analysis::      Representing aliased loads and stores.
* Memory model::        Memory model used by the middle-end.
@end menu

@node Annotations
@section Annotations
@cindex annotations

The optimizers need to associate attributes with variables during the
optimization process.  For instance, we need to know whether a
variable has aliases.  All these attributes are stored in data
structures called annotations which are then linked to the field
@code{ann} in @code{struct tree_common}.


@node SSA Operands
@section SSA Operands
@cindex operands
@cindex virtual operands
@cindex real operands
@findex update_stmt

Almost every GIMPLE statement will contain a reference to a variable
or memory location.  Since statements come in different shapes and
sizes, their operands are going to be located at various spots inside
the statement's tree.  To facilitate access to the statement's
operands, they are organized into lists associated inside each
statement's annotation.  Each element in an operand list is a pointer
to a @code{VAR_DECL}, @code{PARM_DECL} or @code{SSA_NAME} tree node.
This provides a very convenient way of examining and replacing
operands.

Data flow analysis and optimization is done on all tree nodes
representing variables.  Any node for which @code{SSA_VAR_P} returns
nonzero is considered when scanning statement operands.  However, not
all @code{SSA_VAR_P} variables are processed in the same way.  For the
purposes of optimization, we need to distinguish between references to
local scalar variables and references to globals, statics, structures,
arrays, aliased variables, etc.  The reason is simple, the compiler
can gather complete data flow information for a local scalar.  On the
other hand, a global variable may be modified by a function call, it
may not be possible to keep track of all the elements of an array or
the fields of a structure, etc.

The operand scanner gathers two kinds of operands: @dfn{real} and
@dfn{virtual}.  An operand for which @code{is_gimple_reg} returns true
is considered real, otherwise it is a virtual operand.  We also
distinguish between uses and definitions.  An operand is used if its
value is loaded by the statement (e.g., the operand at the RHS of an
assignment).  If the statement assigns a new value to the operand, the
operand is considered a definition (e.g., the operand at the LHS of
an assignment).

Virtual and real operands also have very different data flow
properties.  Real operands are unambiguous references to the
full object that they represent.  For instance, given

@smallexample
@{
  int a, b;
  a = b
@}
@end smallexample

Since @code{a} and @code{b} are non-aliased locals, the statement
@code{a = b} will have one real definition and one real use because
variable @code{a} is completely modified with the contents of
variable @code{b}.  Real definition are also known as @dfn{killing
definitions}.  Similarly, the use of @code{b} reads all its bits.

In contrast, virtual operands are used with variables that can have
a partial or ambiguous reference.  This includes structures, arrays,
globals, and aliased variables.  In these cases, we have two types of
definitions.  For globals, structures, and arrays, we can determine from
a statement whether a variable of these types has a killing definition.
If the variable does, then the statement is marked as having a
@dfn{must definition} of that variable.  However, if a statement is only
defining a part of the variable (i.e.@: a field in a structure), or if we
know that a statement might define the variable but we cannot say for sure,
then we mark that statement as having a @dfn{may definition}.  For
instance, given

@smallexample
@{
  int a, b, *p;

  if (@dots{})
    p = &a;
  else
    p = &b;
  *p = 5;
  return *p;
@}
@end smallexample

The assignment @code{*p = 5} may be a definition of @code{a} or
@code{b}.  If we cannot determine statically where @code{p} is
pointing to at the time of the store operation, we create virtual
definitions to mark that statement as a potential definition site for
@code{a} and @code{b}.  Memory loads are similarly marked with virtual
use operands.  Virtual operands are shown in tree dumps right before
the statement that contains them.  To request a tree dump with virtual
operands, use the @option{-vops} option to @option{-fdump-tree}:

@smallexample
@{
  int a, b, *p;

  if (@dots{})
    p = &a;
  else
    p = &b;
  # a = VDEF <a>
  # b = VDEF <b>
  *p = 5;

  # VUSE <a>
  # VUSE <b>
  return *p;
@}
@end smallexample

Notice that @code{VDEF} operands have two copies of the referenced
variable.  This indicates that this is not a killing definition of
that variable.  In this case we refer to it as a @dfn{may definition}
or @dfn{aliased store}.  The presence of the second copy of the
variable in the @code{VDEF} operand will become important when the
function is converted into SSA form.  This will be used to link all
the non-killing definitions to prevent optimizations from making
incorrect assumptions about them.

Operands are updated as soon as the statement is finished via a call
to @code{update_stmt}.  If statement elements are changed via
@code{SET_USE} or @code{SET_DEF}, then no further action is required
(i.e., those macros take care of updating the statement).  If changes
are made by manipulating the statement's tree directly, then a call
must be made to @code{update_stmt} when complete.  Calling one of the
@code{bsi_insert} routines or @code{bsi_replace} performs an implicit
call to @code{update_stmt}.

@subsection Operand Iterators And Access Routines
@cindex Operand Iterators
@cindex Operand Access Routines

Operands are collected by @file{tree-ssa-operands.c}.  They are stored
inside each statement's annotation and can be accessed through either the
operand iterators or an access routine.

The following access routines are available for examining operands:

@enumerate
@item @code{SINGLE_SSA_@{USE,DEF,TREE@}_OPERAND}: These accessors will return
NULL unless there is exactly one operand matching the specified flags.  If
there is exactly one operand, the operand is returned as either a @code{tree},
@code{def_operand_p}, or @code{use_operand_p}.

@smallexample
tree t = SINGLE_SSA_TREE_OPERAND (stmt, flags);
use_operand_p u = SINGLE_SSA_USE_OPERAND (stmt, SSA_ALL_VIRTUAL_USES);
def_operand_p d = SINGLE_SSA_DEF_OPERAND (stmt, SSA_OP_ALL_DEFS);
@end smallexample

@item @code{ZERO_SSA_OPERANDS}: This macro returns true if there are no
operands matching the specified flags.

@smallexample
if (ZERO_SSA_OPERANDS (stmt, SSA_OP_ALL_VIRTUALS))
  return;
@end smallexample

@item @code{NUM_SSA_OPERANDS}: This macro Returns the number of operands
matching 'flags'.  This actually executes a loop to perform the count, so
only use this if it is really needed.

@smallexample
int count = NUM_SSA_OPERANDS (stmt, flags)
@end smallexample
@end enumerate


If you wish to iterate over some or all operands, use the
@code{FOR_EACH_SSA_@{USE,DEF,TREE@}_OPERAND} iterator.  For example, to print
all the operands for a statement:

@smallexample
void
print_ops (tree stmt)
@{
  ssa_op_iter;
  tree var;

  FOR_EACH_SSA_TREE_OPERAND (var, stmt, iter, SSA_OP_ALL_OPERANDS)
    print_generic_expr (stderr, var, TDF_SLIM);
@}
@end smallexample


How to choose the appropriate iterator:

@enumerate
@item Determine whether you are need to see the operand pointers, or just the
trees, and choose the appropriate macro:

@smallexample
Need            Macro:
----            -------
use_operand_p   FOR_EACH_SSA_USE_OPERAND
def_operand_p   FOR_EACH_SSA_DEF_OPERAND
tree            FOR_EACH_SSA_TREE_OPERAND
@end smallexample

@item You need to declare a variable of the type you are interested
in, and an ssa_op_iter structure which serves as the loop controlling
variable.

@item Determine which operands you wish to use, and specify the flags of
those you are interested in.  They are documented in
@file{tree-ssa-operands.h}:

@smallexample
#define SSA_OP_USE              0x01    /* @r{Real USE operands.}  */
#define SSA_OP_DEF              0x02    /* @r{Real DEF operands.}  */
#define SSA_OP_VUSE             0x04    /* @r{VUSE operands.}  */
#define SSA_OP_VDEF             0x08    /* @r{VDEF operands.}  */

/* @r{These are commonly grouped operand flags.}  */
#define SSA_OP_VIRTUAL_USES	(SSA_OP_VUSE)
#define SSA_OP_VIRTUAL_DEFS	(SSA_OP_VDEF)
#define SSA_OP_ALL_VIRTUALS     (SSA_OP_VIRTUAL_USES | SSA_OP_VIRTUAL_DEFS)
#define SSA_OP_ALL_USES		(SSA_OP_VIRTUAL_USES | SSA_OP_USE)
#define SSA_OP_ALL_DEFS		(SSA_OP_VIRTUAL_DEFS | SSA_OP_DEF)
#define SSA_OP_ALL_OPERANDS	(SSA_OP_ALL_USES | SSA_OP_ALL_DEFS)
@end smallexample
@end enumerate

So if you want to look at the use pointers for all the @code{USE} and
@code{VUSE} operands, you would do something like:

@smallexample
  use_operand_p use_p;
  ssa_op_iter iter;

  FOR_EACH_SSA_USE_OPERAND (use_p, stmt, iter, (SSA_OP_USE | SSA_OP_VUSE))
    @{
      process_use_ptr (use_p);
    @}
@end smallexample

The @code{TREE} macro is basically the same as the @code{USE} and
@code{DEF} macros, only with the use or def dereferenced via
@code{USE_FROM_PTR (use_p)} and @code{DEF_FROM_PTR (def_p)}.  Since we
aren't using operand pointers, use and defs flags can be mixed.

@smallexample
  tree var;
  ssa_op_iter iter;

  FOR_EACH_SSA_TREE_OPERAND (var, stmt, iter, SSA_OP_VUSE)
    @{
       print_generic_expr (stderr, var, TDF_SLIM);
    @}
@end smallexample

@code{VDEF}s are broken into two flags, one for the
@code{DEF} portion (@code{SSA_OP_VDEF}) and one for the USE portion
(@code{SSA_OP_VUSE}).

There are many examples in the code, in addition to the documentation
in @file{tree-ssa-operands.h} and @file{ssa-iterators.h}.

There are also a couple of variants on the stmt iterators regarding PHI
nodes.

@code{FOR_EACH_PHI_ARG} Works exactly like
@code{FOR_EACH_SSA_USE_OPERAND}, except it works over @code{PHI} arguments
instead of statement operands.

@smallexample
/* Look at every virtual PHI use.  */
FOR_EACH_PHI_ARG (use_p, phi_stmt, iter, SSA_OP_VIRTUAL_USES)
@{
   my_code;
@}

/* Look at every real PHI use.  */
FOR_EACH_PHI_ARG (use_p, phi_stmt, iter, SSA_OP_USES)
  my_code;

/* Look at every PHI use.  */
FOR_EACH_PHI_ARG (use_p, phi_stmt, iter, SSA_OP_ALL_USES)
  my_code;
@end smallexample

@code{FOR_EACH_PHI_OR_STMT_@{USE,DEF@}} works exactly like
@code{FOR_EACH_SSA_@{USE,DEF@}_OPERAND}, except it will function on
either a statement or a @code{PHI} node.  These should be used when it is
appropriate but they are not quite as efficient as the individual
@code{FOR_EACH_PHI} and @code{FOR_EACH_SSA} routines.

@smallexample
FOR_EACH_PHI_OR_STMT_USE (use_operand_p, stmt, iter, flags)
  @{
     my_code;
  @}

FOR_EACH_PHI_OR_STMT_DEF (def_operand_p, phi, iter, flags)
  @{
     my_code;
  @}
@end smallexample

@subsection Immediate Uses
@cindex Immediate Uses

Immediate use information is now always available.  Using the immediate use
iterators, you may examine every use of any @code{SSA_NAME}. For instance,
to change each use of @code{ssa_var} to @code{ssa_var2} and call fold_stmt on
each stmt after that is done:

@smallexample
  use_operand_p imm_use_p;
  imm_use_iterator iterator;
  tree ssa_var, stmt;


  FOR_EACH_IMM_USE_STMT (stmt, iterator, ssa_var)
    @{
      FOR_EACH_IMM_USE_ON_STMT (imm_use_p, iterator)
        SET_USE (imm_use_p, ssa_var_2);
      fold_stmt (stmt);
    @}
@end smallexample

There are 2 iterators which can be used. @code{FOR_EACH_IMM_USE_FAST} is
used when the immediate uses are not changed, i.e., you are looking at the
uses, but not setting them.

If they do get changed, then care must be taken that things are not changed
under the iterators, so use the @code{FOR_EACH_IMM_USE_STMT} and
@code{FOR_EACH_IMM_USE_ON_STMT} iterators.  They attempt to preserve the
sanity of the use list by moving all the uses for a statement into
a controlled position, and then iterating over those uses.  Then the
optimization can manipulate the stmt when all the uses have been
processed.  This is a little slower than the FAST version since it adds a
placeholder element and must sort through the list a bit for each statement.
This placeholder element must be also be removed if the loop is
terminated early; a destructor takes care of that when leaving the
@code{FOR_EACH_IMM_USE_STMT} scope.

There are checks in @code{verify_ssa} which verify that the immediate use list
is up to date, as well as checking that an optimization didn't break from the
loop without using this macro.  It is safe to simply 'break'; from a
@code{FOR_EACH_IMM_USE_FAST} traverse.

Some useful functions and macros:
@enumerate
@item  @code{has_zero_uses (ssa_var)} : Returns true if there are no uses of
@code{ssa_var}.
@item   @code{has_single_use (ssa_var)} : Returns true if there is only a
single use of @code{ssa_var}.
@item   @code{single_imm_use (ssa_var, use_operand_p *ptr, tree *stmt)} :
Returns true if there is only a single use of @code{ssa_var}, and also returns
the use pointer and statement it occurs in, in the second and third parameters.
@item   @code{num_imm_uses (ssa_var)} : Returns the number of immediate uses of
@code{ssa_var}. It is better not to use this if possible since it simply
utilizes a loop to count the uses.
@item  @code{PHI_ARG_INDEX_FROM_USE (use_p)} : Given a use within a @code{PHI}
node, return the index number for the use.  An assert is triggered if the use
isn't located in a @code{PHI} node.
@item  @code{USE_STMT (use_p)} : Return the statement a use occurs in.
@end enumerate

Note that uses are not put into an immediate use list until their statement is
actually inserted into the instruction stream via a @code{bsi_*} routine.

It is also still possible to utilize lazy updating of statements, but this
should be used only when absolutely required.  Both alias analysis and the
dominator optimizations currently do this.

When lazy updating is being used, the immediate use information is out of date
and cannot be used reliably.  Lazy updating is achieved by simply marking
statements modified via calls to @code{gimple_set_modified} instead of
@code{update_stmt}.  When lazy updating is no longer required, all the
modified statements must have @code{update_stmt} called in order to bring them
up to date.  This must be done before the optimization is finished, or
@code{verify_ssa} will trigger an abort.

This is done with a simple loop over the instruction stream:
@smallexample
  block_stmt_iterator bsi;
  basic_block bb;
  FOR_EACH_BB (bb)
    @{
      for (bsi = bsi_start (bb); !bsi_end_p (bsi); bsi_next (&bsi))
        update_stmt_if_modified (bsi_stmt (bsi));
    @}
@end smallexample

@node SSA
@section Static Single Assignment
@cindex SSA
@cindex static single assignment

Most of the tree optimizers rely on the data flow information provided
by the Static Single Assignment (SSA) form.  We implement the SSA form
as described in @cite{R. Cytron, J. Ferrante, B. Rosen, M. Wegman, and
K. Zadeck.  Efficiently Computing Static Single Assignment Form and the
Control Dependence Graph.  ACM Transactions on Programming Languages
and Systems, 13(4):451-490, October 1991}.

The SSA form is based on the premise that program variables are
assigned in exactly one location in the program.  Multiple assignments
to the same variable create new versions of that variable.  Naturally,
actual programs are seldom in SSA form initially because variables
tend to be assigned multiple times.  The compiler modifies the program
representation so that every time a variable is assigned in the code,
a new version of the variable is created.  Different versions of the
same variable are distinguished by subscripting the variable name with
its version number.  Variables used in the right-hand side of
expressions are renamed so that their version number matches that of
the most recent assignment.

We represent variable versions using @code{SSA_NAME} nodes.  The
renaming process in @file{tree-ssa.c} wraps every real and
virtual operand with an @code{SSA_NAME} node which contains
the version number and the statement that created the
@code{SSA_NAME}.  Only definitions and virtual definitions may
create new @code{SSA_NAME} nodes.

@cindex PHI nodes
Sometimes, flow of control makes it impossible to determine the
most recent version of a variable.  In these cases, the compiler
inserts an artificial definition for that variable called
@dfn{PHI function} or @dfn{PHI node}.  This new definition merges
all the incoming versions of the variable to create a new name
for it.  For instance,

@smallexample
if (@dots{})
  a_1 = 5;
else if (@dots{})
  a_2 = 2;
else
  a_3 = 13;

# a_4 = PHI <a_1, a_2, a_3>
return a_4;
@end smallexample

Since it is not possible to determine which of the three branches
will be taken at runtime, we don't know which of @code{a_1},
@code{a_2} or @code{a_3} to use at the return statement.  So, the
SSA renamer creates a new version @code{a_4} which is assigned
the result of ``merging'' @code{a_1}, @code{a_2} and @code{a_3}.
Hence, PHI nodes mean ``one of these operands.  I don't know
which''.

The following functions can be used to examine PHI nodes

@defun gimple_phi_result (@var{phi})
Returns the @code{SSA_NAME} created by PHI node @var{phi} (i.e.,
@var{phi}'s LHS)@.
@end defun

@defun gimple_phi_num_args (@var{phi})
Returns the number of arguments in @var{phi}.  This number is exactly
the number of incoming edges to the basic block holding @var{phi}@.
@end defun

@defun gimple_phi_arg (@var{phi}, @var{i})
Returns @var{i}th argument of @var{phi}@.
@end defun

@defun gimple_phi_arg_edge (@var{phi}, @var{i})
Returns the incoming edge for the @var{i}th argument of @var{phi}.
@end defun

@defun gimple_phi_arg_def (@var{phi}, @var{i})
Returns the @code{SSA_NAME} for the @var{i}th argument of @var{phi}.
@end defun


@subsection Preserving the SSA form
@findex update_ssa
@cindex preserving SSA form
Some optimization passes make changes to the function that
invalidate the SSA property.  This can happen when a pass has
added new symbols or changed the program so that variables that
were previously aliased aren't anymore.  Whenever something like this
happens, the affected symbols must be renamed into SSA form again.
Transformations that emit new code or replicate existing statements
will also need to update the SSA form@.

Since GCC implements two different SSA forms for register and virtual
variables, keeping the SSA form up to date depends on whether you are
updating register or virtual names.  In both cases, the general idea
behind incremental SSA updates is similar: when new SSA names are
created, they typically are meant to replace other existing names in
the program@.

For instance, given the following code:

@smallexample
     1  L0:
     2  x_1 = PHI (0, x_5)
     3  if (x_1 < 10)
     4    if (x_1 > 7)
     5      y_2 = 0
     6    else
     7      y_3 = x_1 + x_7
     8    endif
     9    x_5 = x_1 + 1
     10   goto L0;
     11 endif
@end smallexample

Suppose that we insert new names @code{x_10} and @code{x_11} (lines
@code{4} and @code{8})@.

@smallexample
     1  L0:
     2  x_1 = PHI (0, x_5)
     3  if (x_1 < 10)
     4    x_10 = @dots{}
     5    if (x_1 > 7)
     6      y_2 = 0
     7    else
     8      x_11 = @dots{}
     9      y_3 = x_1 + x_7
     10   endif
     11   x_5 = x_1 + 1
     12   goto L0;
     13 endif
@end smallexample

We want to replace all the uses of @code{x_1} with the new definitions
of @code{x_10} and @code{x_11}.  Note that the only uses that should
be replaced are those at lines @code{5}, @code{9} and @code{11}.
Also, the use of @code{x_7} at line @code{9} should @emph{not} be
replaced (this is why we cannot just mark symbol @code{x} for
renaming)@.

Additionally, we may need to insert a PHI node at line @code{11}
because that is a merge point for @code{x_10} and @code{x_11}.  So the
use of @code{x_1} at line @code{11} will be replaced with the new PHI
node.  The insertion of PHI nodes is optional.  They are not strictly
necessary to preserve the SSA form, and depending on what the caller
inserted, they may not even be useful for the optimizers@.

Updating the SSA form is a two step process.  First, the pass has to
identify which names need to be updated and/or which symbols need to
be renamed into SSA form for the first time.  When new names are
introduced to replace existing names in the program, the mapping
between the old and the new names are registered by calling
@code{register_new_name_mapping} (note that if your pass creates new
code by duplicating basic blocks, the call to @code{tree_duplicate_bb}
will set up the necessary mappings automatically).

After the replacement mappings have been registered and new symbols
marked for renaming, a call to @code{update_ssa} makes the registered
changes.  This can be done with an explicit call or by creating
@code{TODO} flags in the @code{tree_opt_pass} structure for your pass.
There are several @code{TODO} flags that control the behavior of
@code{update_ssa}:

@itemize @bullet
@item @code{TODO_update_ssa}.  Update the SSA form inserting PHI nodes
for newly exposed symbols and virtual names marked for updating.
When updating real names, only insert PHI nodes for a real name
@code{O_j} in blocks reached by all the new and old definitions for
@code{O_j}.  If the iterated dominance frontier for @code{O_j}
is not pruned, we may end up inserting PHI nodes in blocks that
have one or more edges with no incoming definition for
@code{O_j}.  This would lead to uninitialized warnings for
@code{O_j}'s symbol@.

@item @code{TODO_update_ssa_no_phi}.  Update the SSA form without
inserting any new PHI nodes at all.  This is used by passes that
have either inserted all the PHI nodes themselves or passes that
need only to patch use-def and def-def chains for virtuals
(e.g., DCE)@.


@item @code{TODO_update_ssa_full_phi}.  Insert PHI nodes everywhere
they are needed.  No pruning of the IDF is done.  This is used
by passes that need the PHI nodes for @code{O_j} even if it
means that some arguments will come from the default definition
of @code{O_j}'s symbol (e.g., @code{pass_linear_transform})@.

WARNING: If you need to use this flag, chances are that your
pass may be doing something wrong.  Inserting PHI nodes for an
old name where not all edges carry a new replacement may lead to
silent codegen errors or spurious uninitialized warnings@.

@item @code{TODO_update_ssa_only_virtuals}.  Passes that update the
SSA form on their own may want to delegate the updating of
virtual names to the generic updater.  Since FUD chains are
easier to maintain, this simplifies the work they need to do.
NOTE: If this flag is used, any OLD->NEW mappings for real names
are explicitly destroyed and only the symbols marked for
renaming are processed@.
@end itemize

@subsection Examining @code{SSA_NAME} nodes
@cindex examining SSA_NAMEs

The following macros can be used to examine @code{SSA_NAME} nodes

@defmac SSA_NAME_DEF_STMT (@var{var})
Returns the statement @var{s} that creates the @code{SSA_NAME}
@var{var}.  If @var{s} is an empty statement (i.e., @code{IS_EMPTY_STMT
(@var{s})} returns @code{true}), it means that the first reference to
this variable is a USE or a VUSE@.
@end defmac

@defmac SSA_NAME_VERSION (@var{var})
Returns the version number of the @code{SSA_NAME} object @var{var}.
@end defmac


@subsection Walking the dominator tree

@deftypefn {Tree SSA function} void walk_dominator_tree (@var{walk_data}, @var{bb})

This function walks the dominator tree for the current CFG calling a
set of callback functions defined in @var{struct dom_walk_data} in
@file{domwalk.h}.  The call back functions you need to define give you
hooks to execute custom code at various points during traversal:

@enumerate
@item Once to initialize any local data needed while processing
@var{bb} and its children.  This local data is pushed into an
internal stack which is automatically pushed and popped as the
walker traverses the dominator tree.

@item Once before traversing all the statements in the @var{bb}.

@item Once for every statement inside @var{bb}.

@item Once after traversing all the statements and before recursing
into @var{bb}'s dominator children.

@item It then recurses into all the dominator children of @var{bb}.

@item After recursing into all the dominator children of @var{bb} it
can, optionally, traverse every statement in @var{bb} again
(i.e., repeating steps 2 and 3).

@item Once after walking the statements in @var{bb} and @var{bb}'s
dominator children.  At this stage, the block local data stack
is popped.
@end enumerate
@end deftypefn

@node Alias analysis
@section Alias analysis
@cindex alias
@cindex flow-sensitive alias analysis
@cindex flow-insensitive alias analysis

Alias analysis in GIMPLE SSA form consists of two pieces.  First
the virtual SSA web ties conflicting memory accesses and provides
a SSA use-def chain and SSA immediate-use chains for walking
possibly dependent memory accesses.  Second an alias-oracle can
be queried to disambiguate explicit and implicit memory references.

@enumerate
@item Memory SSA form.

All statements that may use memory have exactly one accompanied use of
a virtual SSA name that represents the state of memory at the
given point in the IL.

All statements that may define memory have exactly one accompanied
definition of a virtual SSA name using the previous state of memory
and defining the new state of memory after the given point in the IL.

@smallexample
int i;
int foo (void)
@{
  # .MEM_3 = VDEF <.MEM_2(D)>
  i = 1;
  # VUSE <.MEM_3>
  return i;
@}
@end smallexample

The virtual SSA names in this case are @code{.MEM_2(D)} and
@code{.MEM_3}.  The store to the global variable @code{i}
defines @code{.MEM_3} invalidating @code{.MEM_2(D)}.  The
load from @code{i} uses that new state @code{.MEM_3}.

The virtual SSA web serves as constraints to SSA optimizers
preventing illegitimate code-motion and optimization.  It
also provides a way to walk related memory statements.

@item Points-to and escape analysis.

Points-to analysis builds a set of constraints from the GIMPLE
SSA IL representing all pointer operations and facts we do
or do not know about pointers.  Solving this set of constraints
yields a conservatively correct solution for each pointer
variable in the program (though we are only interested in
SSA name pointers) as to what it may possibly point to.

This points-to solution for a given SSA name pointer is stored
in the @code{pt_solution} sub-structure of the
@code{SSA_NAME_PTR_INFO} record.  The following accessor
functions are available:

@itemize @bullet
@item @code{pt_solution_includes}
@item @code{pt_solutions_intersect}
@end itemize

Points-to analysis also computes the solution for two special
set of pointers, @code{ESCAPED} and @code{CALLUSED}.  Those
represent all memory that has escaped the scope of analysis
or that is used by pure or nested const calls.

@item Type-based alias analysis

Type-based alias analysis is frontend dependent though generic
support is provided by the middle-end in @code{alias.c}.  TBAA
code is used by both tree optimizers and RTL optimizers.

Every language that wishes to perform language-specific alias analysis
should define a function that computes, given a @code{tree}
node, an alias set for the node.  Nodes in different alias sets are not
allowed to alias.  For an example, see the C front-end function
@code{c_get_alias_set}.

@item Tree alias-oracle

The tree alias-oracle provides means to disambiguate two memory
references and memory references against statements.  The following
queries are available:

@itemize @bullet
@item @code{refs_may_alias_p}
@item @code{ref_maybe_used_by_stmt_p}
@item @code{stmt_may_clobber_ref_p}
@end itemize

In addition to those two kind of statement walkers are available
walking statements related to a reference ref.
@code{walk_non_aliased_vuses} walks over dominating memory defining
statements and calls back if the statement does not clobber ref
providing the non-aliased VUSE.  The walk stops at
the first clobbering statement or if asked to.
@code{walk_aliased_vdefs} walks over dominating memory defining
statements and calls back on each statement clobbering ref
providing its aliasing VDEF.  The walk stops if asked to.

@end enumerate


@node Memory model
@section Memory model
@cindex memory model

The memory model used by the middle-end models that of the C/C++
languages.  The middle-end has the notion of an effective type
of a memory region which is used for type-based alias analysis.

The following is a refinement of ISO C99 6.5/6, clarifying the block copy case
to follow common sense and extending the concept of a dynamic effective
type to objects with a declared type as required for C++.

@smallexample
The effective type of an object for an access to its stored value is
the declared type of the object or the effective type determined by
a previous store to it.  If a value is stored into an object through
an lvalue having a type that is not a character type, then the
type of the lvalue becomes the effective type of the object for that
access and for subsequent accesses that do not modify the stored value.
If a value is copied into an object using @code{memcpy} or @code{memmove},
or is copied as an array of character type, then the effective type
of the modified object for that access and for subsequent accesses that
do not modify the value is undetermined.  For all other accesses to an
object, the effective type of the object is simply the type of the
lvalue used for the access.
@end smallexample