[PATCH] X86 cost model: Adjust cost for custom lowered vector multiplies

[Arnold Schwaighofer]

Got an offline LGTM from Nadav.

Committed as 176403.

On Mar 1, 2013, at 5:54 PM, Arnold Schwaighofer <aschwaighofer at apple.com> wrote:

> 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
> 
> <0001-X86-cost-model-Adjust-cost-for-custom-lowered-vector.patch>
> 
> 
> 
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