Improved/fixed cost modeling for shuffles by providing masks, improved
cost model for non-identity insertelements.
Differential Revision: https://reviews.llvm.org/D115462
Improved/fixed cost modeling for shuffles by providing masks, improved
cost model for non-identity insertelements.
Differential Revision: https://reviews.llvm.org/D115462
Improved/fixed cost modeling for shuffles by providing masks, improved
cost model for non-identity insertelements.
Differential Revision: https://reviews.llvm.org/D115462
Extractelement instructions may come from different basic blocks, need
to take it into account when looking for a last instruction in the
bundle to prevent compiler crash.
Differential Revision: https://reviews.llvm.org/D126777
Need to handle a corner case correctly, if all elements are Undefs/Poisons,
need to emit actual values, not just poisons.
Differential Revision: https://reviews.llvm.org/D126298
ScatterVectorize nodes should be handled same way as gathers in
reorderBottomToTop function, since we can simple reorder the loads in
this node. Because of that need to include such nodes to the list of
gathered nodes to fix compiler crash.
Differential Revision: https://reviews.llvm.org/D126378
SLP should build ScatterVectorize nodes only if they actually end up
with masked gather rather than with scalarization. In the second
scenario better to build a gather node.
Differential Revision: https://reviews.llvm.org/D126379
Need to use all ReductionOps when propagating flags for the reduction
ops, otherwise transformation is not correct. Plus, need to drop nuw/nsw
flags.
Differential Revision: https://reviews.llvm.org/D126371
The crash is caused by incorrect order set by reorderBottomToTop(), which
happens when it is reordering a TreeEntry which has a user that has already been
reordered earlier. Please see the detailed description in the lit test.
Differential Revision: https://reviews.llvm.org/D126099
SLP vectorizer emits extracts for externally used vectorized scalars and
estimates the cost for each such extract. But in many cases these
scalars are input for insertelement instructions, forming buildvector,
and instead of extractelement/insertelement pair we can emit/cost
estimate shuffle(s) cost and generate series of shuffles, which can be
further optimized.
Tested using test-suite (+SPEC2017), the tests passed, SLP was able to
generate/vectorize more instructions in many cases and it allowed to reduce
number of re-vectorization attempts (where we could try to vectorize
buildector insertelements again and again).
Differential Revision: https://reviews.llvm.org/D107966
This reverts commit fc9c59c355.
The patch triggers an assertion when building SPEC on X86. Reduced
reproducer shared at D107966.
Also reverts follow-up commit 11a09af76d.
SLP vectorizer emits extracts for externally used vectorized scalars and
estimates the cost for each such extract. But in many cases these
scalars are input for insertelement instructions, forming buildvector,
and instead of extractelement/insertelement pair we can emit/cost
estimate shuffle(s) cost and generate series of shuffles, which can be
further optimized.
Tested using test-suite (+SPEC2017), the tests passed, SLP was able to
generate/vectorize more instructions in many cases and it allowed to reduce
number of re-vectorization attempts (where we could try to vectorize
buildector insertelements again and again).
Differential Revision: https://reviews.llvm.org/D107966
When we generate a horizontal reduction of floating adds fed by a vectorized
tree rooted at floating multiplies, we should account for the cost of no
longer being able to generate scalar FMAs. Similarly, if we vectorize a
list of floating multiplies that each feeds a single floating add, we should
again account for this cost.
The first test was reduced from a case where the vectorizable tree looked
barely profitable (cost -1) with a horizontal reduction, but produced
substantially worse code than allowing the FMAs to be generated. The second
test was derived from the first: we again generate a horizontal reduction
here, but even if the horizontal reduction is forced to be unprofitable, we
try to vectorize the multiplies. I have follow-up patches to address these
issues.
Differential Revision: https://reviews.llvm.org/D124867
The pattern matching and vectgorization for reductions was not very
effective. Some of of the possible reduction values were marked as
external arguments, SLP could not find some reduction patterns because
of too early attempt to vectorize pair of binops arguments, the cost of
consts reductions was not correct. Patch addresses these issues and
improves the analysis/cost estimation and vectorization of the
reductions.
The most significant changes in SLP.NumVectorInstructions:
Metric: SLP.NumVectorInstructions [140/14396]
Program results results0 diff
test-suite :: SingleSource/Benchmarks/Adobe-C++/loop_unroll.test 920.00 3548.00 285.7%
test-suite :: SingleSource/Benchmarks/BenchmarkGame/n-body.test 66.00 122.00 84.8%
test-suite :: MultiSource/Benchmarks/DOE-ProxyApps-C/miniGMG/miniGMG.test 100.00 128.00 28.0%
test-suite :: MultiSource/Benchmarks/Prolangs-C/TimberWolfMC/timberwolfmc.test 664.00 810.00 22.0%
test-suite :: MultiSource/Benchmarks/mafft/pairlocalalign.test 592.00 687.00 16.0%
test-suite :: MultiSource/Benchmarks/MiBench/consumer-lame/consumer-lame.test 402.00 426.00 6.0%
test-suite :: MultiSource/Applications/JM/lencod/lencod.test 1665.00 1745.00 4.8%
test-suite :: External/SPEC/CINT2017rate/500.perlbench_r/500.perlbench_r.test 135.00 139.00 3.0%
test-suite :: External/SPEC/CINT2017speed/600.perlbench_s/600.perlbench_s.test 135.00 139.00 3.0%
test-suite :: MultiSource/Benchmarks/7zip/7zip-benchmark.test 388.00 397.00 2.3%
test-suite :: MultiSource/Applications/JM/ldecod/ldecod.test 895.00 914.00 2.1%
test-suite :: MultiSource/Benchmarks/MiBench/telecomm-gsm/telecomm-gsm.test 240.00 244.00 1.7%
test-suite :: MultiSource/Benchmarks/mediabench/gsm/toast/toast.test 240.00 244.00 1.7%
test-suite :: External/SPEC/CINT2017speed/602.gcc_s/602.gcc_s.test 820.00 832.00 1.5%
test-suite :: External/SPEC/CINT2017rate/502.gcc_r/502.gcc_r.test 820.00 832.00 1.5%
test-suite :: External/SPEC/CFP2017rate/526.blender_r/526.blender_r.test 14804.00 14914.00 0.7%
test-suite :: MultiSource/Benchmarks/Bullet/bullet.test 8125.00 8183.00 0.7%
test-suite :: External/SPEC/CINT2017speed/625.x264_s/625.x264_s.test 1330.00 1338.00 0.6%
test-suite :: External/SPEC/CINT2017rate/525.x264_r/525.x264_r.test 1330.00 1338.00 0.6%
test-suite :: External/SPEC/CFP2017rate/510.parest_r/510.parest_r.test 9832.00 9880.00 0.5%
test-suite :: External/SPEC/CFP2017rate/511.povray_r/511.povray_r.test 5267.00 5291.00 0.5%
test-suite :: External/SPEC/CFP2017rate/538.imagick_r/538.imagick_r.test 4018.00 4024.00 0.1%
test-suite :: External/SPEC/CFP2017speed/638.imagick_s/638.imagick_s.test 4018.00 4024.00 0.1%
test-suite :: External/SPEC/CFP2017speed/644.nab_s/644.nab_s.test 426.00 424.00 -0.5%
test-suite :: External/SPEC/CFP2017rate/544.nab_r/544.nab_r.test 426.00 424.00 -0.5%
test-suite :: External/SPEC/CINT2017rate/541.leela_r/541.leela_r.test 201.00 192.00 -4.5%
test-suite :: External/SPEC/CINT2017speed/641.leela_s/641.leela_s.test 201.00 192.00 -4.5%
644.nab_s and 544.nab_r - reduced number of shuffles but increased number
of useful vectorized instructions.
641.leela_s and 541.leela_r - the function
`@_ZN9FastBoard25get_pattern3_augment_specEiib` is not inlined anymore
but its body gets vectorized successfully. Before, the function was
inlined twice and vectorized just after inlining, currently it is not
required. The vector code looks pretty similar, just like as it was before.
Differential Revision: https://reviews.llvm.org/D111574
Need to check if the reduction is still (not)cmp-select pattern min/max
reduction to avoid compiler crash during building list of reduction
operations. cmp-sel pattern provides 2 reduction operations, while
intrinsics - just one.
If alternate node has only 2 instructions and the tree is already big
enough, better to skip the vectorization of such nodes, they are not
very profitable (the resulting code cotains 3 instructions instead of
original 2 scalars). SLP can try to vectorize the buildvector sequence
in the next attempt, if it is profitable.
Metric: SLP.NumVectorInstructions
Program SLP.NumVectorInstructions
results results0 diff
test-suite :: MultiSource/Benchmarks/DOE-ProxyApps-C/miniAMR/miniAMR.test 72.00 73.00 1.4%
test-suite :: MultiSource/Benchmarks/Prolangs-C/TimberWolfMC/timberwolfmc.test 1186.00 1198.00 1.0%
test-suite :: MultiSource/Benchmarks/DOE-ProxyApps-C++/miniFE/miniFE.test 241.00 242.00 0.4%
test-suite :: MultiSource/Applications/JM/lencod/lencod.test 2131.00 2139.00 0.4%
test-suite :: External/SPEC/CINT2017rate/523.xalancbmk_r/523.xalancbmk_r.test 6377.00 6384.00 0.1%
test-suite :: External/SPEC/CINT2017speed/623.xalancbmk_s/623.xalancbmk_s.test 6377.00 6384.00 0.1%
test-suite :: External/SPEC/CFP2017rate/510.parest_r/510.parest_r.test 12650.00 12658.00 0.1%
test-suite :: External/SPEC/CFP2017rate/526.blender_r/526.blender_r.test 26169.00 26147.00 -0.1%
test-suite :: MultiSource/Benchmarks/Trimaran/enc-3des/enc-3des.test 99.00 86.00 -13.1%
Gains:
526.blender_r - more vectorized trees.
enc-3des - same.
Others:
510.parest_r - no changes.
miniFE - same
623.xalancbmk_s - some (non-profitable) parts of the trees are not
vectorized.
523.xalancbmk_r - same
lencod - same
timberwolfmc - same
miniAMR - same
Differential Revision: https://reviews.llvm.org/D125571
If the insert indes was used already or is not constant, we should stop
looking for unique buildvector sequence, it mustbe splitted to
2 different buildvectors.
This is to support 0950d4060c.
External users that can affect reordering, with range == VL.size() but
non consecutive (like stores to A[0],A[0],A[3],A[3]) would escape the check
for consecutive accesses and would cause a crash.
Further improvement of the cost model for the scalars used in
buildvectors sequences. The main functionality is outlined into
a separate function.
The cost is calculated in the following way:
1. If the Base vector is not undef vector, resizing the very first mask to
have common VF and perform action for 2 input vectors (including non-undef
Base). Other shuffle masks are combined with the resulting after the 1 stage and processed as a shuffle of 2 elements.
2. If the Base is undef vector and have only 1 shuffle mask, perform the
action only for 1 vector with the given mask, if it is not the identity
mask.
3. If > 2 masks are used, perform serie of shuffle actions for 2 vectors,
combing the masks properly between the steps.
The original implementation misses the very first analysis for the Base
vector, so the cost might too optimistic in some cases. But it improves
the cost for the insertelements which are part of the current SLP graph.
Part of D107966.
Differential Revision: https://reviews.llvm.org/D115750
The current reordering scheme only checks the ordering of in-tree operands.
There are some cases, however, where we need to adjust the ordering based on
the ordering of a future SLP-tree who's instructions are not part of the
current tree, but are external users.
This patch is a simple implementation of this. We keep track of scalar stores
that are users of TreeEntries and if they look profitable to vectorize, then
we keep track of their ordering. During the reordering step we take this new
index order into account. This can remove some shuffles in cases like in the
lit test.
Differential Revision: https://reviews.llvm.org/D125111
Looks like this test exposes some non-determinism and fails with
auth-generated checks. Recommited with sole check it didn't crash.
Will investigate why it happens.
If the same scalar is inserted several times into the same buildvector,
the mask index can be used already. In this case need to check, that
this scalar is already part of the vectorized buildvector.
We can try to vectorize number of stores less than MinVecRegSize
/ scalar_value_size, if it is allowed by target. Gives an extra
opportunity for the vectorization.
Fixes PR54985.
Differential Revision: https://reviews.llvm.org/D124284
Need to use actual index instead of the tree entry position, since the
insert index may be different than 0. It mean, that we vectorized part
of the buildvector starting from not initial insertelement instruction
beause of some reason.
Given a load without a better order, this patch partially sorts the
elements to form clusters of adjacent elements in memory. These clusters
can potentially be loaded in fewer loads, meaning less overall shuffling
(for example loading v4i8 clusters of a v16i8 as a single f32 loads, as
opposed to multiple independent bytes loads and inserts).
Differential Revision: https://reviews.llvm.org/D122145
Based off the script from D103695, on AVX1, Jaguar/Bulldozer both have low throughput for ymm select patterns (BLENDV + OR(AND,ANDN))), and even on AVX2 Haswell still struggles with BLENDV ops
Further improvement of the cost model for the scalars used in
buildvectors sequences. The main functionality is outlined into
a separate function.
The cost is calculated in the following way:
1. If the Base vector is not undef vector, resizing the very first mask to
have common VF and perform action for 2 input vectors (including non-undef
Base). Other shuffle masks are combined with the resulting after the 1 stage and processed as a shuffle of 2 elements.
2. If the Base is undef vector and have only 1 shuffle mask, perform the
action only for 1 vector with the given mask, if it is not the identity
mask.
3. If > 2 masks are used, perform serie of shuffle actions for 2 vectors,
combing the masks properly between the steps.
The original implementation misses the very first analysis for the Base
vector, so the cost might too optimistic in some cases. But it improves
the cost for the insertelements which are part of the current SLP graph.
Part of D107966.
Differential Revision: https://reviews.llvm.org/D115750
