blob: 08965cc5e2029cc2d9b3208f68cb7bbd0a7dde55 [file] [log] [blame]
! { dg-do compile }
! { dg-require-effective-target vect_double }
! { dg-options "-O3 --param vect-max-peeling-for-alignment=0 -fpredictive-commoning -fdump-tree-pcom-details -std=legacy" }
! { dg-additional-options "-mprefer-avx128" { target { i?86-*-* x86_64-*-* } } }
! { dg-additional-options "-mzarch" { target { s390*-*-* } } }
******* RESID COMPUTES THE RESIDUAL: R = V - AU
*
* THIS SIMPLE IMPLEMENTATION COSTS 27A + 4M PER RESULT, WHERE
* A AND M DENOTE THE COSTS OF ADDITION (OR SUBTRACTION) AND
* MULTIPLICATION, RESPECTIVELY. BY USING SEVERAL TWO-DIMENSIONAL
* BUFFERS ONE CAN REDUCE THIS COST TO 13A + 4M IN THE GENERAL
* CASE, OR 10A + 3M WHEN THE COEFFICIENT A(1) IS ZERO.
*
SUBROUTINE RESID(U,V,R,N,A)
INTEGER N
REAL*8 U(N,N,N),V(N,N,N),R(N,N,N),A(0:3)
INTEGER I3, I2, I1
C
DO 600 I3=2,N-1
DO 600 I2=2,N-1
DO 600 I1=2,N-1
600 R(I1,I2,I3)=V(I1,I2,I3)
> -A(0)*( U(I1, I2, I3 ) )
> -A(1)*( U(I1-1,I2, I3 ) + U(I1+1,I2, I3 )
> + U(I1, I2-1,I3 ) + U(I1, I2+1,I3 )
> + U(I1, I2, I3-1) + U(I1, I2, I3+1) )
> -A(2)*( U(I1-1,I2-1,I3 ) + U(I1+1,I2-1,I3 )
> + U(I1-1,I2+1,I3 ) + U(I1+1,I2+1,I3 )
> + U(I1, I2-1,I3-1) + U(I1, I2+1,I3-1)
> + U(I1, I2-1,I3+1) + U(I1, I2+1,I3+1)
> + U(I1-1,I2, I3-1) + U(I1-1,I2, I3+1)
> + U(I1+1,I2, I3-1) + U(I1+1,I2, I3+1) )
> -A(3)*( U(I1-1,I2-1,I3-1) + U(I1+1,I2-1,I3-1)
> + U(I1-1,I2+1,I3-1) + U(I1+1,I2+1,I3-1)
> + U(I1-1,I2-1,I3+1) + U(I1+1,I2-1,I3+1)
> + U(I1-1,I2+1,I3+1) + U(I1+1,I2+1,I3+1) )
C
RETURN
END
! we want to check that predictive commoning did something on the
! vectorized loop. If vector factor is 2, the vectorized loop can
! be predictive commoned, we check if predictive commoning PHI node
! is created with vector(2) type.
! { dg-final { scan-tree-dump "Executing predictive commoning without unrolling" "pcom" { xfail vect_variable_length } } }
! { dg-final { scan-tree-dump "vectp_u.*__lsm.* = PHI <.*vectp_u.*__lsm" "pcom" { xfail vect_variable_length } } }