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llvm-6502/test/Transforms/LoopVectorize/X86/avx1.ll

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Add support for loops that don't start with Zero. This is important for loops in the LAPACK test-suite. These loops start at 1 because they are auto-converted from fortran. git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@167084 91177308-0d34-0410-b5e6-96231b3b80d8
2012-10-31 00:45:26 +00:00
; RUN: opt < %s -loop-vectorize -mtriple=x86_64-apple-macosx10.8.0 -mcpu=corei7-avx -S | FileCheck %s
target datalayout = "e-p:64:64:64-i1:8:8-i8:8:8-i16:16:16-i32:32:32-i64:64:64-f32:32:32-f64:64:64-v64:64:64-v128:128:128-a0:0:64-s0:64:64-f80:128:128-n8:16:32:64-S128"
target triple = "x86_64-apple-macosx10.8.0"
;CHECK: @read_mod_write_single_ptr
;CHECK: load <8 x float>
;CHECK: ret i32
define i32 @read_mod_write_single_ptr(float* nocapture %a, i32 %n) nounwind uwtable ssp {
%1 = icmp sgt i32 %n, 0
br i1 %1, label %.lr.ph, label %._crit_edge
.lr.ph: ; preds = %0, %.lr.ph
%indvars.iv = phi i64 [ %indvars.iv.next, %.lr.ph ], [ 0, %0 ]
%2 = getelementptr inbounds float* %a, i64 %indvars.iv
%3 = load float* %2, align 4
%4 = fmul float %3, 3.000000e+00
store float %4, float* %2, align 4
%indvars.iv.next = add i64 %indvars.iv, 1
%lftr.wideiv = trunc i64 %indvars.iv.next to i32
%exitcond = icmp eq i32 %lftr.wideiv, %n
br i1 %exitcond, label %._crit_edge, label %.lr.ph
._crit_edge: ; preds = %.lr.ph, %0
ret i32 undef
}
;CHECK: @read_mod_i64
X86 cost model: Adjust cost for custom lowered vector multiplies This matters for example in following matrix multiply: int **mmult(int rows, int cols, int **m1, int **m2, int **m3) { int i, j, k, val; for (i=0; i<rows; i++) { for (j=0; j<cols; j++) { val = 0; for (k=0; k<cols; k++) { val += m1[i][k] * m2[k][j]; } m3[i][j] = val; } } return(m3); } Taken from the test-suite benchmark Shootout. We estimate the cost of the multiply to be 2 while we generate 9 instructions for it and end up being quite a bit slower than the scalar version (48% on my machine). Also, properly differentiate between avx1 and avx2. On avx-1 we still split the vector into 2 128bits and handle the subvector muls like above with 9 instructions. Only on avx-2 will we have a cost of 9 for v4i64. I changed the test case in test/Transforms/LoopVectorize/X86/avx1.ll to use an add instead of a mul because with a mul we now no longer vectorize. I did verify that the mul would be indeed more expensive when vectorized with 3 kernels: for (i ...) r += a[i] * 3; for (i ...) m1[i] = m1[i] * 3; // This matches the test case in avx1.ll and a matrix multiply. In each case the vectorized version was considerably slower. radar://13304919 git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@176403 91177308-0d34-0410-b5e6-96231b3b80d8
2013-03-02 04:02:52 +00:00
;CHECK: load <2 x i64>
Add support for loops that don't start with Zero. This is important for loops in the LAPACK test-suite. These loops start at 1 because they are auto-converted from fortran. git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@167084 91177308-0d34-0410-b5e6-96231b3b80d8
2012-10-31 00:45:26 +00:00
;CHECK: ret i32
define i32 @read_mod_i64(i64* nocapture %a, i32 %n) nounwind uwtable ssp {
%1 = icmp sgt i32 %n, 0
br i1 %1, label %.lr.ph, label %._crit_edge
.lr.ph: ; preds = %0, %.lr.ph
%indvars.iv = phi i64 [ %indvars.iv.next, %.lr.ph ], [ 0, %0 ]
%2 = getelementptr inbounds i64* %a, i64 %indvars.iv
%3 = load i64* %2, align 4
X86 cost model: Adjust cost for custom lowered vector multiplies This matters for example in following matrix multiply: int **mmult(int rows, int cols, int **m1, int **m2, int **m3) { int i, j, k, val; for (i=0; i<rows; i++) { for (j=0; j<cols; j++) { val = 0; for (k=0; k<cols; k++) { val += m1[i][k] * m2[k][j]; } m3[i][j] = val; } } return(m3); } Taken from the test-suite benchmark Shootout. We estimate the cost of the multiply to be 2 while we generate 9 instructions for it and end up being quite a bit slower than the scalar version (48% on my machine). Also, properly differentiate between avx1 and avx2. On avx-1 we still split the vector into 2 128bits and handle the subvector muls like above with 9 instructions. Only on avx-2 will we have a cost of 9 for v4i64. I changed the test case in test/Transforms/LoopVectorize/X86/avx1.ll to use an add instead of a mul because with a mul we now no longer vectorize. I did verify that the mul would be indeed more expensive when vectorized with 3 kernels: for (i ...) r += a[i] * 3; for (i ...) m1[i] = m1[i] * 3; // This matches the test case in avx1.ll and a matrix multiply. In each case the vectorized version was considerably slower. radar://13304919 git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@176403 91177308-0d34-0410-b5e6-96231b3b80d8
2013-03-02 04:02:52 +00:00
%4 = add i64 %3, 3
Add support for loops that don't start with Zero. This is important for loops in the LAPACK test-suite. These loops start at 1 because they are auto-converted from fortran. git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@167084 91177308-0d34-0410-b5e6-96231b3b80d8
2012-10-31 00:45:26 +00:00
store i64 %4, i64* %2, align 4
%indvars.iv.next = add i64 %indvars.iv, 1
%lftr.wideiv = trunc i64 %indvars.iv.next to i32
%exitcond = icmp eq i32 %lftr.wideiv, %n
br i1 %exitcond, label %._crit_edge, label %.lr.ph
._crit_edge: ; preds = %.lr.ph, %0
ret i32 undef
}
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