When computing the smallest and largest types for selecting the maximum
vectorization factor, we currently ignore loads and stores of pointer types if
the memory access is non-consecutive. We do this because such accesses must be
scalarized regardless of vectorization factor, and thus shouldn't be considered
when determining the factor. This patch makes this check less aggressive by
also considering non-consecutive accesses that may be vectorized, such as
interleaved accesses. Because we don't know at the time of the check if an
accesses will certainly be vectorized (this is a cost model decision given a
particular VF), we consider all accesses that can potentially be vectorized.
Differential Revision: https://reviews.llvm.org/D30305
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The practice in LV is that we emit analysis remarks and then finally report
either a missed or applied remark on the final decision whether vectorization
is taking place. On this code path, we were closing with an analysis remark.
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This patch merges the existing floating-point induction variable widening code
into the integer induction variable widening code, creating a single set of
functions for both kinds of inductions. The primary motivation for doing this
is to enable vector phi node creation for floating-point induction variables.
Differential Revision: https://reviews.llvm.org/D30211
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The need for this removed when I converted everything to use the opt-remark
classes directly with the streaming interface.
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The need for this removed when I converted everything to use the opt-remark
classes directly with the streaming interface.
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Prevent memory objects of different address spaces to be part of
the same load/store groups when analysing interleaved accesses.
This is fixing pr31900.
Reviewers: HaoLiu, mssimpso, mkuper
Reviewed By: mssimpso, mkuper
Subscribers: llvm-commits, efriedma, mzolotukhin
Differential Revision: https://reviews.llvm.org/D29717
This reverts r295042 (re-applies r295038) with an additional fix for the
buildbot problem.
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We previously only created a vector phi node for an induction variable if its
step had a constant integer type. However, the step actually only needs to be
loop-invariant. We only handle inductions having loop-invariant steps, so this
patch should enable vector phi node creation for all integer induction
variables that will be vectorized.
Differential Revision: https://reviews.llvm.org/D29956
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This reapplies commit r294967 with a fix for the execution time regressions
caught by the clang-cmake-aarch64-quick bot. We now extend the truncate
optimization to non-primary induction variables only if the truncate isn't
already free.
Differential Revision: https://reviews.llvm.org/D29847
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Prevent memory objects of different address spaces to be part of
the same load/store groups when analysing interleaved accesses.
This is fixing pr31900.
Reviewers: HaoLiu, mssimpso, mkuper
Reviewed By: mssimpso, mkuper
Subscribers: llvm-commits, efriedma, mzolotukhin
Differential Revision: https://reviews.llvm.org/D29717
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This reverts commit r294967. This patch caused execution time slowdowns in a
few LLVM test-suite tests, as reported by the clang-cmake-aarch64-quick bot.
I'm reverting to investigate.
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This patch extends the optimization of truncations whose operand is an
induction variable with a constant integer step. Previously we were only
applying this optimization to the primary induction variable. However, the cost
model assumes the optimization is applied to the truncation of all integer
induction variables (even regardless of step type). The transformation is now
applied to the other induction variables, and I've updated the cost model to
ensure it is better in sync with the transformation we actually perform.
Differential Revision: https://reviews.llvm.org/D29847
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Summary:
This patch starts the implementation as discuss in the following RFC: http://lists.llvm.org/pipermail/llvm-dev/2016-October/106532.html
When optimization duplicates code that will scale down the execution count of a basic block, we will record the duplication factor as part of discriminator so that the offline process tool can find the duplication factor and collect the accurate execution frequency of the corresponding source code. Two important optimization that fall into this category is loop vectorization and loop unroll. This patch records the duplication factor for these 2 optimizations.
The recording will be guarded by a flag encode-duplication-in-discriminators, which is off by default.
Reviewers: probinson, aprantl, davidxl, hfinkel, echristo
Reviewed By: hfinkel
Subscribers: mehdi_amini, anemet, mzolotukhin, llvm-commits
Differential Revision: https://reviews.llvm.org/D26420
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Making the cost model selecting between Interleave, GatherScatter or Scalar vectorization form of memory instruction.
The right decision should be done for non-consecutive memory access instrcuctions that may have more than one vectorization solution.
This patch includes the following changes:
- Cost Model calculates the cost of Load/Store vector form and choose the better option between Widening, Interleave, GatherScactter and Scalarization. Cost Model keeps the widening decision.
- Arrays of Uniform and Scalar values are moved from Legality to Cost Model.
- Cost Model collects Uniforms and Scalars per VF. The collection is based on CM decision map of Loadis/Stores vectorization form.
- Vectorization of memory instruction is performed according to the CM decision.
Differential Revision: https://reviews.llvm.org/D27919
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This patch moves some helper functions related to interleaved access
vectorization out of LoopVectorize.cpp and into VectorUtils.cpp. We would like
to use these functions in a follow-on patch that improves interleaved load and
store lowering in (ARM/AArch64)ISelLowering.cpp. One of the functions was
already duplicated there and has been removed.
Differential Revision: https://reviews.llvm.org/D29398
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By calling getScalarizationOverhead with the CallInst instead of the types of
its arguments, we make sure that only unique call arguments are added to the
scalarization cost.
getScalarizationOverhead() is extended to handle calls by only passing on the
actual call arguments (which is not all the operands).
This also eliminates a wrapper function with the same name.
review: Hal Finkel
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change the set of uniform instructions in the loop causing an assert
failure.
The problem is that the legalization checking also builds data
structures mapping various facts about the loop body. The immediate
cause was the set of uniform instructions. If these then change when
LCSSA is formed, the data structures would already have been built and
become stale. The included test case triggered an assert in loop
vectorize that was reduced out of the new PM's pipeline.
The solution is to form LCSSA early enough that no information is cached
across the changes made. The only really obvious position is outside of
the main logic to vectorize the loop. This also has the advantage of
removing one case where forming LCSSA could mutate the loop but we
wouldn't track that as a "Changed" state.
If it is significantly advantageous to do some legalization checking
prior to this, we can do a more careful positioning but it seemed best
to just back off to a safe position first.
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Refactoring to remove duplications of this method.
New method getOperandsScalarizationOverhead() that looks at the present unique
operands and add extract costs for them. Old behaviour was to just add extract
costs for one operand of the type always, which still happens in
getArithmeticInstrCost() if no operands are provided by the caller.
This is a good start of improving on this, but there are more places
that can be improved by using getOperandsScalarizationOverhead().
Review: Hal Finkel
https://reviews.llvm.org/D29017
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This changes the vectorizer to explicitly use the loopsimplify and lcssa utils,
instead of "requiring" the transformations as if they were analyses.
This is not NFC, since it changes the LCSSA behavior - we no longer run LCSSA
for all loops, but rather only for the loops we expect to modify.
Differential Revision: https://reviews.llvm.org/D28868
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We currently check whether a reduction has a single outside user. We don't
really need to require that - we just need to make sure a single value is
used externally. The number of external users of that value shouldn't actually
matter.
Differential Revision: https://reviews.llvm.org/D28830
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updated instructions:
pmulld, pmullw, pmulhw, mulsd, mulps, mulpd, divss, divps, divsd, divpd, addpd and subpd.
special optimization case which replaces pmulld with pmullw\pmulhw\pshuf seq.
In case if the real operands bitwidth <= 16.
Differential Revision: https://reviews.llvm.org/D28104
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arguments much like the CGSCC pass manager.
This is a major redesign following the pattern establish for the CGSCC layer to
support updates to the set of loops during the traversal of the loop nest and
to support invalidation of analyses.
An additional significant burden in the loop PM is that so many passes require
access to a large number of function analyses. Manually ensuring these are
cached, available, and preserved has been a long-standing burden in LLVM even
with the help of the automatic scheduling in the old pass manager. And it made
the new pass manager extremely unweildy. With this design, we can package the
common analyses up while in a function pass and make them immediately available
to all the loop passes. While in some cases this is unnecessary, I think the
simplicity afforded is worth it.
This does not (yet) address loop simplified form or LCSSA form, but those are
the next things on my radar and I have a clear plan for them.
While the patch is very large, most of it is either mechanically updating loop
passes to the new API or the new testing for the loop PM. The code for it is
reasonably compact.
I have not yet updated all of the loop passes to correctly leverage the update
mechanisms demonstrated in the unittests. I'll do that in follow-up patches
along with improved FileCheck tests for those passes that ensure things work in
more realistic scenarios. In many cases, there isn't much we can do with these
until the loop simplified form and LCSSA form are in place.
Differential Revision: https://reviews.llvm.org/D28292
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This patch delays the fix-up step for external induction variable users until
after the dominator tree has been properly updated. This should fix PR30742.
The SCEVExpander in InductionDescriptor::transform can generate code in the
wrong location if the dominator tree is not up-to-date. We should work towards
keeping the dominator tree up-to-date throughout the transformation.
Reference: https://llvm.org/bugs/show_bug.cgi?id=30742
Differential Revision: https://reviews.llvm.org/D28168
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This code seems to be target dependent which may not be the same for all targets.
Passed the decision whether the given stride is complex or not to the target by sending stride information via SCEV to getAddressComputationCost instead of 'IsComplex'.
Specifically at X86 targets we dont see any significant address computation cost in case of the strided access in general.
Differential Revision: https://reviews.llvm.org/D27518
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This patch reapplies r289863. The original patch was reverted because it
exposed a bug causing the loop vectorizer to crash in the Python runtime on
PPC. The underlying issue was fixed with r289958.
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After r288909, instructions feeding predicated instructions may be scalarized
if profitable. Since these instructions will remain scalar, we shouldn't
attempt to type-shrink them. We should only truncate vector types to their
minimal bit widths. This bug was exposed by enabling the vectorization of loops
containing conditional stores by default.
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stores by default
This uncovers a crasher in the loop vectorizer on PPC when building the
Python runtime. I'll send the testcase to the review thread for the
original commit.
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After r289755, the AssumptionCache is no longer needed. Variables affected by
assumptions are now found by using the new operand-bundle-based scheme. This
new scheme is more computationally efficient, and also we need much less
code...
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This patch attempts to scalarize the operand expressions of predicated
instructions if they were conditionally executed in the original loop. After
scalarization, the expressions will be sunk inside the blocks created for the
predicated instructions. The transformation essentially performs
un-if-conversion on the operands.
The cost model has been updated to determine if scalarization is profitable. It
compares the cost of a vectorized instruction, assuming it will be
if-converted, to the cost of the scalarized instruction, assuming that the
instructions corresponding to each vector lane will be sunk inside a predicated
block, possibly avoiding execution. If it's more profitable to scalarize the
entire expression tree feeding the predicated instruction, the expression will
be scalarized; otherwise, it will be vectorized. We only consider the cost of
the entire expression to accurately estimate the cost of the required
insertelement and extractelement instructions.
Differential Revision: https://reviews.llvm.org/D26083
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The register usage algorithm incorrectly treats instructions whose value is
not used within the loop (e.g. those that do not produce a value).
The algorithm first calculates the usages within the loop. It iterates over
the instructions in order, and records at which instruction index each use
ends (in fact, they're actually recorded against the next index, as this is
when we want to delete them from the open intervals).
The algorithm then iterates over the instructions again, adding each
instruction in turn to a list of open intervals. Instructions are then
removed from the list of open intervals when they occur in the list of uses
ended at the current index.
The problem is, instructions which are not used in the loop are skipped.
However, although they aren't used, the last use of a value may have been
recorded against that instruction index. In this case, the use is not deleted
from the open intervals, which may then bump up the estimated register usage.
This patch fixes the issue by simply moving the "is used" check after the loop
which erases the uses at the current index.
Differential Revision: https://reviews.llvm.org/D26554
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This is PR28376.
Unfortunately given the current structure of optimization diagnostics we
lack the capability to tell whether the user has
passed -Rpass-analysis=loop-vectorize since this is local to the
front-end (BackendConsumer::OptimizationRemarkHandler).
So rather than printing this even if the user has already
passed -Rpass-analysis, this patch just punts and stops recommending
this option. I don't think that getting this right is worth the
complexity.
Differential Revision: https://reviews.llvm.org/D26563
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possible pointer-wrap-around concerns, in some cases.
Before this patch, collectConstStridedAccesses (part of interleaved-accesses
analysis) called getPtrStride with [Assume=false, ShouldCheckWrap=true] when
examining all candidate pointers. This is too conservative. Instead, this
patch makes collectConstStridedAccesses use an optimistic approach, calling
getPtrStride with [Assume=true, ShouldCheckWrap=false], and then, once the
candidate interleave groups have been formed, revisits the pointer-wrapping
analysis but only where it matters: namely, in groups that have gaps, and where
the gaps are not at the very end of the group (in which case the loop is
peeled). This second time getPtrStride is called with [Assume=false,
ShouldCheckWrap=true], but this could further be improved to using Assume=true,
once we also add the logic to track that we are not going to meet the scev
runtime checks threshold.
Differential Revision: https://reviews.llvm.org/D25276
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When we predicate an instruction (div, rem, store) we place the instruction in
its own basic block within the vectorized loop. If a predicated instruction has
scalar operands, it's possible to recursively sink these scalar expressions
into the predicated block so that they might avoid execution. This patch sinks
as much scalar computation as possible into predicated blocks. We previously
were able to sink such operands only if they were extractelement instructions.
Differential Revision: https://reviews.llvm.org/D25632
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