The cost of vector instructions has always been high under AArch64, in order to
add a high cost for inserts/extracts, shuffles and scalarization. This is a
conservative approach to limit the scope of unusual SLP vectorization where the
codegen ends up being quite poor, but has always been higher than the correct
costs would be for any specific core.
This relaxes that, reducing the vector insert/extract cost from 3 to 2. It is a
generalization of D142359 to all AArch64 cpus. The ScalarizationOverhead is
also overridden for integer vector at the same time, to remove the effect of
lane 0 being considered free for integer vectors (something that should only be
true for float when scalarizing).
The lower insert/extract cost will reduce the cost of insert, extracts,
shuffling and scalarization. The adjustments of ScalaizationOverhead will
increase the cost on integer, especially for small vectors. The end result will
be lower cost for float and long-integer types, some higher cost for some
smaller vectors. This, along with the raw insert/extract cost being lower, will
generally mean more vectorization from the Loop and SLP vectorizer.
We may end up regretting this, as that vectorization is not always profitable.
In all the benchmarking I have done this is generally an improvement in the
overall performance, and I've attempted to address the places where it wasn't
with other costmodel adjustments.
Differential Revision: https://reviews.llvm.org/D155459
This lowers the cost for FADD, FSUB, and FNEG. The motivation is to avoid
over-eager SLP vectorisation, that makes it look like SLP vectorisation is
profitable but results in significant slow downs. Lowering the cost for scalar
FADD/FSUB costs helps the profitability decision to favour the scalar
version where vectorisation isn't beneficial.
Lowering the cost for these floating point operations makes sense because a lot
of other instructions including many shuffles have only a cost of 1; these
FADD/FSUB/FNEG instructions should not be twice the cost.
Performance results show a 7% improvement for Imagick from SPEC FP 2017, a
small improvement in Blender, and unchanged results for the other apps in SPEC.
RAJAPerf is neutral and mostly shows no changes.
Differential Revision: https://reviews.llvm.org/D146033
Gather nodes are vectorized as simply vector of the scalars instead of
relying on the actual node. It leads to the fact that in some cases
we may miss incorrect transformation (non-matching set of scalars is
just ended as a gather node instead of possible vector/gather node).
Better to rely on the actual nodes, it allows to improve stability and
better detect missed cases.
Differential Revision: https://reviews.llvm.org/D135174
Gather nodes are vectorized as simply vector of the scalars instead of
relying on the actual node. It leads to the fact that in some cases
we may miss incorrect transformation (non-matching set of scalars is
just ended as a gather node instead of possible vector/gather node).
Better to rely on the actual nodes, it allows to improve stability and
better detect missed cases.
Differential Revision: https://reviews.llvm.org/D135174
If we vectorize a e.g. store, we leave around a bunch of getelementptrs for the individual scalar stores which we removed. We can go ahead and delete them as well.
This is purely for test output quality and readability. It should have no effect in any sane pipeline.
Differential Revision: https://reviews.llvm.org/D122493
The original commit exposed several missing dependencies (e.g. latent bugs in SLP scheduling). Most of these were fixed over the weekend and have had several days to bake. The last was fixed this morning after being noticed in manual review of test changes yesterday. See the review thread for links to each change.
Original commit message follows:
SLP currently schedules all instructions within a scheduling window which stretches from the first instruction potentially vectorized to the last. This window can include a very large number of unrelated instructions which are not being considered for vectorization. This change switches the code to only schedule the sub-graph consisting of the instructions being vectorized and their transitive users.
This has the effect of greatly reducing the amount of work performed in large basic blocks, and thus greatly improves compile time on degenerate examples. To understand the effects, I added some statistics (not planned for upstream contribution). Here's an illustration from my motivating example:
Before this patch:
704357 SLP - Number of calcDeps actions
699021 SLP - Number of schedule calls
5598 SLP - Number of ReSchedule actions
59 SLP - Number of ReScheduleOnFail actions
10084 SLP - Number of schedule resets
8523 SLP - Number of vector instructions generated
After this patch:
102895 SLP - Number of calcDeps actions
161916 SLP - Number of schedule calls
5637 SLP - Number of ReSchedule actions
55 SLP - Number of ReScheduleOnFail actions
10083 SLP - Number of schedule resets
8403 SLP - Number of vector instructions generated
I do want to highlight that there is a small difference in number of generated vector instructions. This example is hitting the bailout due to maximum window size, and the change in scheduling is slightly perturbing when and how we hit it. This can be seen in the RescheduleOnFail counter change. Given that, I think we can safely ignore.
The downside of this change can be seen in the large test diff. We group all vectorizable instructions together at the bottom of the scheduling region. This means that vector instructions can move quite far from their original point in code. While maybe undesirable, I don't see this as being a major problem as this pass is not intended to be a general scheduling pass.
For context, it's worth noting that the pre-scheduling that SLP does while building the vector tree is exactly the sub-graph scheduling implemented by this patch.
Differential Revision: https://reviews.llvm.org/D118538
Root issue which triggered the revert was fixed in 689bab. No changes in the reapplied patch.
Original commit message follows:
SLP currently schedules all instructions within a scheduling window which stretches from the first instr
uction potentially vectorized to the last. This window can include a very large number of unrelated instruct
ions which are not being considered for vectorization. This change switches the code to only schedule the su
b-graph consisting of the instructions being vectorized and their transitive users.
This has the effect of greatly reducing the amount of work performed in large basic blocks, and thus greatly improves compile time on degenerate examples. To understand the effects, I added some statistics (not planned for upstream contribution). Here's an illustration from my motivating example:
Before this patch:
704357 SLP - Number of calcDeps actions
699021 SLP - Number of schedule calls
5598 SLP - Number of ReSchedule actions
59 SLP - Number of ReScheduleOnFail actions
10084 SLP - Number of schedule resets
8523 SLP - Number of vector instructions generated
After this patch:
102895 SLP - Number of calcDeps actions
161916 SLP - Number of schedule calls
5637 SLP - Number of ReSchedule actions
55 SLP - Number of ReScheduleOnFail actions
10083 SLP - Number of schedule resets
8403 SLP - Number of vector instructions generated
I do want to highlight that there is a small difference in number of generated vector instructions. This example is hitting the bailout due to maximum window size, and the change in scheduling is slightly perturbing when and how we hit it. This can be seen in the RescheduleOnFail counter change. Given that, I think we can safely ignore.
The downside of this change can be seen in the large test diff. We group all vectorizable instructions together at the bottom of the scheduling region. This means that vector instructions can move quite far from their original point in code. While maybe undesirable, I don't see this as being a major problem as this pass is not intended to be a general scheduling pass.
For context, it's worth noting that the pre-scheduling that SLP does while building the vector tree is exactly the sub-graph scheduling implemented by this patch.
Differential Revision: https://reviews.llvm.org/D118538
This reverts commit 0539a26d91a1b7c74022fa9cf33bd7faca87544d.
Causes a miscompile, see comments on D118538.
Required updating bottom-to-top-reorder.ll.
SLP currently schedules all instructions within a scheduling window which stretches from the first instruction potentially vectorized to the last. This window can include a very large number of unrelated instructions which are not being considered for vectorization. This change switches the code to only schedule the sub-graph consisting of the instructions being vectorized and their transitive users.
This has the effect of greatly reducing the amount of work performed in large basic blocks, and thus greatly improves compile time on degenerate examples. To understand the effects, I added some statistics (not planned for upstream contribution). Here's an illustration from my motivating example:
Before this patch:
704357 SLP - Number of calcDeps actions
699021 SLP - Number of schedule calls
5598 SLP - Number of ReSchedule actions
59 SLP - Number of ReScheduleOnFail actions
10084 SLP - Number of schedule resets
8523 SLP - Number of vector instructions generated
After this patch:
102895 SLP - Number of calcDeps actions
161916 SLP - Number of schedule calls
5637 SLP - Number of ReSchedule actions
55 SLP - Number of ReScheduleOnFail actions
10083 SLP - Number of schedule resets
8403 SLP - Number of vector instructions generated
I do want to highlight that there is a small difference in number of generated vector instructions. This example is hitting the bailout due to maximum window size, and the change in scheduling is slightly perturbing when and how we hit it. This can be seen in the RescheduleOnFail counter change. Given that, I think we can safely ignore.
The downside of this change can be seen in the large test diff. We group all vectorizable instructions together at the bottom of the scheduling region. This means that vector instructions can move quite far from their original point in code. While maybe undesirable, I don't see this as being a major problem as this pass is not intended to be a general scheduling pass.
For context, it's worth noting that the pre-scheduling that SLP does while building the vector tree is exactly the sub-graph scheduling implemented by this patch.
Differential Revision: https://reviews.llvm.org/D118538
This patch makes SLP and LV emit operations with initial vectors set to poison constant instead of undef.
This is a part of efforts for using poison vector instead of undef to represent "doesn't care" vector.
The goal is to make nice shufflevector optimizations valid that is currently incorrect due to the tricky interaction between undef and poison (see https://bugs.llvm.org/show_bug.cgi?id=44185 ).
Reviewed By: fhahn
Differential Revision: https://reviews.llvm.org/D94061
As it's causing some bot failures (and per request from kbarton).
This reverts commit r358543/ab70da07286e618016e78247e4a24fcb84077fda.
llvm-svn: 358546
official Git repository.
Remove the directions for using git-svn, and demote the prominence of
the svn instructions.
Also, fix a few other issues while I'm in there:
* Mention LLVM_ENABLE_PROJECTS more.
* Getting started doesn't need to mention test-suite, but should
mention clang and the other projects.
* Remove mentions of "configure", since that's long gone.
I've also adjusted a few other mentions of svn to point to github, but
have not done so comprehensively.
Differential Revision: https://reviews.llvm.org/D56654
llvm-svn: 351130
This is a slight reduction of one of the benchmarks
that suffered with D43079. Cost model changes should
not cause this test to remain scalarized.
llvm-svn: 325717
