Commit Graph

4 Commits

Author SHA1 Message Date
Anton Afanasyev ab2c499d3a [SLP] Add insertelement instructions to vectorizable tree
Add new type of tree node for `InsertElementInst` chain forming vector.
These instructions could be either removed, or replaced by shuffles during
vectorization and we can add this node to cost model, so naturally estimating
their cost, getting rid of `CompensateCost` tricks and reducing further work
for InstCombine. This fixes PR40522 and PR35732 in a natural way. Also this
patch is the first step towards revectorization of partially vectorization
(to fix PR42022 completely). After adding inserts to tree the next step is
to add vector instructions there (for instance, to merge `store <2 x float>`
and `store <2 x float>` to `store <4 x float>`).

Fixes PR40522 and PR35732.

Differential Revision: https://reviews.llvm.org/D98714
2021-05-13 07:41:45 +03:00
Anton Afanasyev 00a0595b25 [SLP][Test] Fix and precommit tests for D98714 2021-05-13 07:41:06 +03:00
Stanislav Mekhanoshin 87d7757bbe [SLP] Control maximum vectorization factor from TTI
D82227 has added a proper check to limit PHI vectorization to the
maximum vector register size. That unfortunately resulted in at
least a couple of regressions on SystemZ and x86.

This change reverts PHI handling from D82227 and replaces it with
a more general check in SLPVectorizerPass::tryToVectorizeList().
Moved to tryToVectorizeList() it allows to restart vectorization
if initial chunk fails.

However, this function is more general and handles not only PHI
but everything which SLP handles. If vectorization factor would
be limited to maximum vector register size it would limit much
more vectorization than before leading to further regressions.
Therefore a new TTI callback getMaximumVF() is added with the
default 0 to preserve current behavior and limit nothing. Then
targets can decide what is better for them.

The callback gets ElementSize just like a similar getMinimumVF()
function and the main opcode of the chain. The latter is to avoid
regressions at least on the AMDGPU. We can have loads and stores
up to 128 bit wide, and <2 x 16> bit vector math on some
subtargets, where the rest shall not be vectorized. I.e. we need
to differentiate based on the element size and operation itself.

Differential Revision: https://reviews.llvm.org/D92059
2020-12-14 08:49:40 -08:00
Matt Arsenault 66073953a5 AMDGPU: Allow vectorization of round intrinsic
There seems to be a small benefit to the legalized sequence for v2f16
round with packed instructions, so allow vectorizing it by reducing
the cost.

An unintended side effect is vectorization of f32 round also
happens. The current FMA logic seems off to me, and isn't checking for
packed instructions.
2020-03-23 17:00:41 -04:00