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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
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@@ -27,7 +27,7 @@ define i32 @read_mod_write_single_ptr(float* nocapture %a, i32 %n) nounwind uwta
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;CHECK: @read_mod_i64
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;CHECK: load <4 x i64>
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;CHECK: load <2 x i64>
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;CHECK: ret i32
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define i32 @read_mod_i64(i64* nocapture %a, i32 %n) nounwind uwtable ssp {
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%1 = icmp sgt i32 %n, 0
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@@ -37,7 +37,7 @@ define i32 @read_mod_i64(i64* nocapture %a, i32 %n) nounwind uwtable ssp {
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%indvars.iv = phi i64 [ %indvars.iv.next, %.lr.ph ], [ 0, %0 ]
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%2 = getelementptr inbounds i64* %a, i64 %indvars.iv
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%3 = load i64* %2, align 4
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%4 = mul i64 %3, 3
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%4 = add i64 %3, 3
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store i64 %4, i64* %2, align 4
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%indvars.iv.next = add i64 %indvars.iv, 1
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%lftr.wideiv = trunc i64 %indvars.iv.next to i32
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