Arnold Schwaighofer 5f0d9dbdf4 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
..
ARM	ARM cost model: Add vector reverse shuffle costs	2013-02-12 02:40:39 +00:00
PowerPC	Refine fix to bug 15041.	2013-02-08 18:19:17 +00:00
X86	X86 cost model: Adjust cost for custom lowered vector multiplies	2013-03-02 04:02:52 +00:00
lit.local.cfg
no_info.ll	Make opt grab the triple from the module and use it to initialize the target machine.	2013-01-01 08:00:32 +00:00