Summary:
vectorizer-maximize-bandwidth is generally useful in terms of performance. I've tested the impact of changing this to default on speccpu benchmarks on sandybridge machines. The result shows non-negative impact:
spec/2006/fp/C++/444.namd 26.84 -0.31%
spec/2006/fp/C++/447.dealII 46.19 +0.89%
spec/2006/fp/C++/450.soplex 42.92 -0.44%
spec/2006/fp/C++/453.povray 38.57 -2.25%
spec/2006/fp/C/433.milc 24.54 -0.76%
spec/2006/fp/C/470.lbm 41.08 +0.26%
spec/2006/fp/C/482.sphinx3 47.58 -0.99%
spec/2006/int/C++/471.omnetpp 22.06 +1.87%
spec/2006/int/C++/473.astar 22.65 -0.12%
spec/2006/int/C++/483.xalancbmk 33.69 +4.97%
spec/2006/int/C/400.perlbench 33.43 +1.70%
spec/2006/int/C/401.bzip2 23.02 -0.19%
spec/2006/int/C/403.gcc 32.57 -0.43%
spec/2006/int/C/429.mcf 40.35 +0.27%
spec/2006/int/C/445.gobmk 26.96 +0.06%
spec/2006/int/C/456.hmmer 24.4 +0.19%
spec/2006/int/C/458.sjeng 27.91 -0.08%
spec/2006/int/C/462.libquantum 57.47 -0.20%
spec/2006/int/C/464.h264ref 46.52 +1.35%
geometric mean +0.29%
The regression on 453.povray seems real, but is due to secondary effects as all hot functions are bit-identical with and without the flag.
I started this patch to consult upstream opinions on this. It will be greatly appreciated if the community can help test the performance impact of this change on other architectures so that we can decided if this should be target-dependent.
Reviewers: hfinkel, mkuper, davidxl, chandlerc
Reviewed By: chandlerc
Subscribers: rengolin, sanjoy, javed.absar, bjope, dorit, magabari, RKSimon, llvm-commits, mzolotukhin
Differential Revision: https://reviews.llvm.org/D33341
llvm-svn: 306336
Instead of providing access to the internal MapStorage holding all Values
associated with a given Key, used for setting or resetting them all together,
ValueMap keeps its MapStorage internal; its new interface allows getting,
setting or resetting a single Value, per part or per part-and-lane.
Follows the discussion in https://reviews.llvm.org/D32871.
Differential Revision: https://reviews.llvm.org/D34473
llvm-svn: 306331
Summary:
vectorizer-maximize-bandwidth is generally useful in terms of performance. I've tested the impact of changing this to default on speccpu benchmarks on sandybridge machines. The result shows non-negative impact:
spec/2006/fp/C++/444.namd 26.84 -0.31%
spec/2006/fp/C++/447.dealII 46.19 +0.89%
spec/2006/fp/C++/450.soplex 42.92 -0.44%
spec/2006/fp/C++/453.povray 38.57 -2.25%
spec/2006/fp/C/433.milc 24.54 -0.76%
spec/2006/fp/C/470.lbm 41.08 +0.26%
spec/2006/fp/C/482.sphinx3 47.58 -0.99%
spec/2006/int/C++/471.omnetpp 22.06 +1.87%
spec/2006/int/C++/473.astar 22.65 -0.12%
spec/2006/int/C++/483.xalancbmk 33.69 +4.97%
spec/2006/int/C/400.perlbench 33.43 +1.70%
spec/2006/int/C/401.bzip2 23.02 -0.19%
spec/2006/int/C/403.gcc 32.57 -0.43%
spec/2006/int/C/429.mcf 40.35 +0.27%
spec/2006/int/C/445.gobmk 26.96 +0.06%
spec/2006/int/C/456.hmmer 24.4 +0.19%
spec/2006/int/C/458.sjeng 27.91 -0.08%
spec/2006/int/C/462.libquantum 57.47 -0.20%
spec/2006/int/C/464.h264ref 46.52 +1.35%
geometric mean +0.29%
The regression on 453.povray seems real, but is due to secondary effects as all hot functions are bit-identical with and without the flag.
I started this patch to consult upstream opinions on this. It will be greatly appreciated if the community can help test the performance impact of this change on other architectures so that we can decided if this should be target-dependent.
Reviewers: hfinkel, mkuper, davidxl, chandlerc
Reviewed By: chandlerc
Subscribers: rengolin, sanjoy, javed.absar, bjope, dorit, magabari, RKSimon, llvm-commits, mzolotukhin
Differential Revision: https://reviews.llvm.org/D33341
llvm-svn: 305960
Summary:
Existing heuristic uses the ratio between the function entry
frequency and the loop invocation frequency to find cold loops. However,
even if the loop executes frequently, if it has a small trip count per
each invocation, vectorization is not beneficial. On the other hand,
even if the loop invocation frequency is much smaller than the function
invocation frequency, if the trip count is high it is still beneficial
to vectorize the loop.
This patch uses estimated trip count computed from the profile metadata
as a primary metric to determine coldness of the loop. If the estimated
trip count cannot be computed, it falls back to the original heuristics.
Reviewers: Ayal, mssimpso, mkuper, danielcdh, wmi, tejohnson
Reviewed By: tejohnson
Subscribers: tejohnson, mzolotukhin, llvm-commits
Differential Revision: https://reviews.llvm.org/D32451
llvm-svn: 305729
If we're shrinking a binary operation, it may be the case that the new
operations wraps where the old didn't. If this happens, the behavior
should be well-defined. So, we can't always carry wrapping flags with us
when we shrink operations.
If we do, we get incorrect optimizations in cases like:
void foo(const unsigned char *from, unsigned char *to, int n) {
for (int i = 0; i < n; i++)
to[i] = from[i] - 128;
}
which gets optimized to:
void foo(const unsigned char *from, unsigned char *to, int n) {
for (int i = 0; i < n; i++)
to[i] = from[i] | 128;
}
Because:
- InstCombine turned `sub i32 %from.i, 128` into
`add nuw nsw i32 %from.i, 128`.
- LoopVectorize vectorized the add to be `add nuw nsw <16 x i8>` with a
vector full of `i8 128`s
- InstCombine took advantage of the fact that the newly-shrunken add
"couldn't wrap", and changed the `add` to an `or`.
InstCombine seems happy to figure out whether we can add nuw/nsw on its
own, so I just decided to drop the flags. There are already a number of
places in LoopVectorize where we rely on InstCombine to clean up.
llvm-svn: 305053
I did this a long time ago with a janky python script, but now
clang-format has built-in support for this. I fed clang-format every
line with a #include and let it re-sort things according to the precise
LLVM rules for include ordering baked into clang-format these days.
I've reverted a number of files where the results of sorting includes
isn't healthy. Either places where we have legacy code relying on
particular include ordering (where possible, I'll fix these separately)
or where we have particular formatting around #include lines that
I didn't want to disturb in this patch.
This patch is *entirely* mechanical. If you get merge conflicts or
anything, just ignore the changes in this patch and run clang-format
over your #include lines in the files.
Sorry for any noise here, but it is important to keep these things
stable. I was seeing an increasing number of patches with irrelevant
re-ordering of #include lines because clang-format was used. This patch
at least isolates that churn, makes it easy to skip when resolving
conflicts, and gets us to a clean baseline (again).
llvm-svn: 304787
Following the request made in https://reviews.llvm.org/D32871,
scalarizeInstruction() which is no longer overridden by InnerLoopUnroller is
hereby made non-virtual in InnerLoopVectorizer.
Should have been part of r297580 originally.
llvm-svn: 304685
Fixed some comments, added an additional description of the algorithms,
improved readability of the code.
Differential revision: https://reviews.llvm.org/D33320
llvm-svn: 304616
Summary:
Fixed some comments, added an additional description of the algorithms,
improved readability of the code.
Reviewers: anemet
Subscribers: llvm-commits, mzolotukhin
Differential Revision: https://reviews.llvm.org/D33320
llvm-svn: 304593
r303763 caused build failures in some out-of-tree tests due to an assertion in
TTI. The original patch updated cost estimates for induction variable update
instructions marked for scalarization. However, it didn't consider that the
incoming value of an induction variable phi node could be a cast instruction.
This caused queries for cast instruction costs with a mix of vector and scalar
types. This patch includes a fix for cast instructions and the test case from
PR33193.
The fix was suggested by Jonas Paulsson <paulsson@linux.vnet.ibm.com>.
Reference: https://bugs.llvm.org/show_bug.cgi?id=33193
Original Differential Revision: https://reviews.llvm.org/D33457
llvm-svn: 304235
For non-uniform instructions marked for scalarization, we should update
`VectorTy` when computing instruction costs to reflect the scalar type. In
addition to determining instruction costs, this type is also used to signal
that all instructions in the loop will be scalarized. This currently affects
memory instructions and non-pointer induction variables and their updates. (We
also mark GEPs scalar after vectorization, but their cost is computed together
with memory instructions.) For scalarized induction updates, this patch also
scales the scalar cost by the vectorization factor, corresponding to each
induction step.
llvm-svn: 303763
The loop vectorizer usually vectorizes any instruction it can and then
extracts the elements for a scalarized use. On SystemZ, all elements
containing addresses must be extracted into address registers (GRs). Since
this extraction is not free, it is better to have the address in a suitable
register to begin with. By forcing address arithmetic instructions and loads
of addresses to be scalar after vectorization, two benefits result:
* No need to extract the register
* LSR optimizations trigger (LSR isn't handling vector addresses currently)
Benchmarking show improvements on SystemZ with this new behaviour.
Any other target could try this by returning false in the new hook
prefersVectorizedAddressing().
Review: Renato Golin, Elena Demikhovsky, Ulrich Weigand
https://reviews.llvm.org/D32422
llvm-svn: 303744
The default behavior of -Rpass-analysis=loop-vectorizer is to report only the
first reason encountered for not vectorizing, if one is found, at which time the
vectorizer aborts its handling of the loop. This patch allows multiple reasons
for not vectorizing to be identified and reported, at the potential expense of
additional compile-time, under allowExtraAnalysis which can currently be turned
on by Clang's -fsave-optimization-record and opt's -pass-remarks-missed.
Removed from LoopVectorizationLegality::canVectorize() the redundant checking
and reporting if we CantComputeNumberOfIterations, as LAI::canAnalyzeLoop() also
does that. This redundancy is caught by a lit test once multiple reasons are
reported.
Patch initially developed by Dror Barak.
Differential Revision: https://reviews.llvm.org/D33396
llvm-svn: 303613
Summary:
Implements PR889
Removing the virtual table pointer from Value saves 1% of RSS when doing
LTO of llc on Linux. The impact on time was positive, but too noisy to
conclusively say that performance improved. Here is a link to the
spreadsheet with the original data:
https://docs.google.com/spreadsheets/d/1F4FHir0qYnV0MEp2sYYp_BuvnJgWlWPhWOwZ6LbW7W4/edit?usp=sharing
This change makes it invalid to directly delete a Value, User, or
Instruction pointer. Instead, such code can be rewritten to a null check
and a call Value::deleteValue(). Value objects tend to have their
lifetimes managed through iplist, so for the most part, this isn't a big
deal. However, there are some places where LLVM deletes values, and
those places had to be migrated to deleteValue. I have also created
llvm::unique_value, which has a custom deleter, so it can be used in
place of std::unique_ptr<Value>.
I had to add the "DerivedUser" Deleter escape hatch for MemorySSA, which
derives from User outside of lib/IR. Code in IR cannot include MemorySSA
headers or call the MemoryAccess object destructors without introducing
a circular dependency, so we need some level of indirection.
Unfortunately, no class derived from User may have any virtual methods,
because adding a virtual method would break User::getHungOffOperands(),
which assumes that it can find the use list immediately prior to the
User object. I've added a static_assert to the appropriate OperandTraits
templates to help people avoid this trap.
Reviewers: chandlerc, mehdi_amini, pete, dberlin, george.burgess.iv
Reviewed By: chandlerc
Subscribers: krytarowski, eraman, george.burgess.iv, mzolotukhin, Prazek, nlewycky, hans, inglorion, pcc, tejohnson, dberlin, llvm-commits
Differential Revision: https://reviews.llvm.org/D31261
llvm-svn: 303362
ARM Neon has native support for half-sized vector registers (64 bits). This
is beneficial for example for 2D and 3D graphics. This patch adds the option
to lower MinVecRegSize from 128 via a TTI in the SLP Vectorizer.
*** Performance Analysis
This change was motivated by some internal benchmarks but it is also
beneficial on SPEC and the LLVM testsuite.
The results are with -O3 and PGO. A negative percentage is an improvement.
The testsuite was run with a sample size of 4.
** SPEC
* CFP2006/482.sphinx3 -3.34%
A pretty hot loop is SLP vectorized resulting in nice instruction reduction.
This used to be a +22% regression before rL299482.
* CFP2000/177.mesa -3.34%
* CINT2000/256.bzip2 +6.97%
My current plan is to extend the fix in rL299482 to i16 which brings the
regression down to +2.5%. There are also other problems with the codegen in
this loop so there is further room for improvement.
** LLVM testsuite
* SingleSource/Benchmarks/Misc/ReedSolomon -10.75%
There are multiple small SLP vectorizations outside the hot code. It's a bit
surprising that it adds up to 10%. Some of this may be code-layout noise.
* MultiSource/Benchmarks/VersaBench/beamformer/beamformer -8.40%
The opt-viewer screenshot can be seen at F3218284. We start at a colder store
but the tree leads us into the hottest loop.
* MultiSource/Applications/lambda-0.1.3/lambda -2.68%
* MultiSource/Benchmarks/Bullet/bullet -2.18%
This is using 3D vectors.
* SingleSource/Benchmarks/Shootout-C++/Shootout-C++-lists +6.67%
Noise, binary is unchanged.
* MultiSource/Benchmarks/Ptrdist/anagram/anagram +4.90%
There is an additional SLP in the cold code. The test runs for ~1sec and
prints out over 2000 lines. This is most likely noise.
* MultiSource/Applications/aha/aha +1.63%
* MultiSource/Applications/JM/lencod/lencod +1.41%
* SingleSource/Benchmarks/Misc/richards_benchmark +1.15%
Differential Revision: https://reviews.llvm.org/D31965
llvm-svn: 303116
This patch adds min/max population count, leading/trailing zero/one bit counting methods.
The min methods return answers based on bits that are known without considering unknown bits. The max methods give answers taking into account the largest count that unknown bits could give.
Differential Revision: https://reviews.llvm.org/D32931
llvm-svn: 302925
The approach I followed was to emit the remark after getTreeCost concludes
that SLP is profitable. I initially tried emitting them after the
vectorizeRootInstruction calls in vectorizeChainsInBlock but I vaguely
remember missing a few cases for example in HorizontalReduction::tryToReduce.
ORE is placed in BoUpSLP so that it's available from everywhere (notably
HorizontalReduction::tryToReduce).
We use the first instruction in the root bundle as the locator for the remark.
In order to get a sense how far the tree is spanning I've include the size of
the tree in the remark. This is not perfect of course but it gives you at
least a rough idea about the tree. Then you can follow up with -view-slp-tree
to really see the actual tree.
llvm-svn: 302811
Introduce LoopVectorizationPlanner.executePlan(), replacing ILV.vectorize() and
refactoring ILV.vectorizeLoop(). Method collectDeadInstructions() is moved from
ILV to LVP. These changes facilitate building VPlans and using them to generate
code, following https://reviews.llvm.org/D28975 and its tentative breakdown.
Method ILV.createEmptyLoop() is renamed ILV.createVectorizedLoopSkeleton() to
improve clarity; it's contents remain intact.
Differential Revision: https://reviews.llvm.org/D32200
llvm-svn: 302790
The AArch64 instruction set has a few "widening" instructions (e.g., uaddl,
saddl, uaddw, etc.) that take one or more doubleword operands and produce
quadword results. The operands are automatically sign- or zero-extended as
appropriate. However, in LLVM IR, these extends are explicit. This patch
updates TTI to consider these widening instructions as single operations whose
cost is attached to the arithmetic instruction. It marks extends that are part
of a widening operation "free" and applies a sub-target specified overhead
(zero by default) to the arithmetic instructions.
Differential Revision: https://reviews.llvm.org/D32706
llvm-svn: 302582
Summary:
In first order recurrence vectorization, when the previous value is a phi node, we need to
set the insertion point to the first non-phi node.
We can have the previous value being a phi node, due to the generation of new
IVs as part of trunc optimization [1].
[1] https://reviews.llvm.org/rL294967
Reviewers: mssimpso, mkuper
Subscribers: mzolotukhin, llvm-commits
Differential Revision: https://reviews.llvm.org/D32969
llvm-svn: 302532
- This change allows targets to opt-in to using them instead of the log2
shufflevector algorithm.
- The SLP and Loop vectorizers have the common code to do shuffle reductions
factored out into LoopUtils, and now have a unified interface for generating
reductions regardless of the preference of the target. LoopUtils now uses TTI
to determine what kind of reductions the target wants to handle.
- For CodeGen, basic legalization support is added.
Differential Revision: https://reviews.llvm.org/D30086
llvm-svn: 302514
This patch introduces a new KnownBits struct that wraps the two APInt used by computeKnownBits. This allows us to treat them as more of a unit.
Initially I've just altered the signatures of computeKnownBits and InstCombine's simplifyDemandedBits to pass a KnownBits reference instead of two separate APInt references. I'll do similar to the SelectionDAG version of computeKnownBits/simplifyDemandedBits as a separate patch.
I've added a constructor that allows initializing both APInts to the same bit width with a starting value of 0. This reduces the repeated pattern of initializing both APInts. Once place default constructed the APInts so I added a default constructor for those cases.
Going forward I would like to add more methods that will work on the pairs. For example trunc, zext, and sext occur on both APInts together in several places. We should probably add a clear method that can be used to clear both pieces. Maybe a method to check for conflicting information. A method to return (Zero|One) so we don't write it out everywhere. Maybe a method for (Zero|One).isAllOnesValue() to determine if all bits are known. I'm sure there are many other methods we can come up with.
Differential Revision: https://reviews.llvm.org/D32376
llvm-svn: 301432
Commits were:
"Use WeakVH instead of WeakTrackingVH in AliasSetTracker's UnkownInsts"
"Add a new WeakVH value handle; NFC"
"Rename WeakVH to WeakTrackingVH; NFC"
The changes assumed pointers are 8 byte aligned on all architectures.
llvm-svn: 301429
Summary:
I plan to use WeakVH to mean "nulls itself out on deletion, but does
not track RAUW" in a subsequent commit.
Reviewers: dblaikie, davide
Reviewed By: davide
Subscribers: arsenm, mehdi_amini, mcrosier, mzolotukhin, jfb, llvm-commits, nhaehnle
Differential Revision: https://reviews.llvm.org/D32266
llvm-svn: 301424
This patch uses various APInt methods to reduce temporary APInt creation.
This should be all of the unrelated cleanups that got buried in D32376(creating a KnownBits struct) as well as some pointed out by Simon during the review of that. Plus a few improvements to use counting instead of masking.
I've left out any places where we do something like (KnownZero & KnownOne) != 0 as I plan to add a helper method to KnownBits to ask that question and didn't want to thrash that code an additional time.
Differential Revision: https://reviews.llvm.org/D32495
llvm-svn: 301338
This patch is part of D28975's breakdown.
Genreating the control-flow to guard predicated instructions modified to
only use SplitBlockAndInsertIfThen() for producing the if-then construct.
Differential Revision: https://reviews.llvm.org/D32224
llvm-svn: 301293
Phi nodes in non-header blocks are converted to select instructions after
if-conversion. This patch updates the cost model to account for the selects.
Differential Revision: https://reviews.llvm.org/D31906
llvm-svn: 300980
In tryToVectorizeList, under a very limited circumstance (when entered
from tryToVectorizePair), the values may be reordered (swapped) and the
SLP tree is built with the new order. This extends that to the case when
starting from phis in vectorizeChainsInBlock when there are exactly two
phis. The textual order of phi nodes shouldn't really matter. Without
this change, the loop body in the accompnaying test case is fully vectorized
when we swap the orde of the phis but not with this order. While this
doesn't solve the phi-ordering problem in a general way (for more than 2
phis), this is simple fix that piggybacks on an existing mechanism and
is useful in cases like multiplying two complex numbers.
Differential revision: https://reviews.llvm.org/D32065
llvm-svn: 300574
This patch is part of D28975's breakdown.
Add caching for block masks similar to the cache already used for edge masks,
replacing generation per user with reusing the first generated value which
dominates all uses.
Differential Revision: https://reviews.llvm.org/D32054
llvm-svn: 300557
This patch is part of D28975's breakdown - no change in output intended.
LV's code currently assumes the vectorized loop is a single basic block up
until predicateInstructions() is called. This patch removes two manifestations
of this assumption (loop phi incoming values, dominator tree update) by
replacing the use of vectorLoopBody with the vectorized loop's latch/header.
Differential Revision: https://reviews.llvm.org/D32040
llvm-svn: 300310
Summary:
In first order recurrences where phi's are used outside the loop,
we should generate an additional vector.extract of the second last element from
the vectorized phi update.
This is because we require the phi itself (which is the value at the second last
iteration of the vector loop) and not the phi's update within the loop.
Also fix the code gen when we just unroll, but don't vectorize.
Fixes PR32396.
Reviewers: mssimpso, mkuper, anemet
Subscribers: llvm-commits, mzolotukhin
Differential Revision: https://reviews.llvm.org/D31979
llvm-svn: 300238
Refactoring InnerLoopVectorizer's vectorizeBlockInLoop() to provide
vectorizeInstruction(). Aligning DeadInstructions with its only user.
Facilitates driving the transformation by VPlan - follows
https://reviews.llvm.org/D28975 and its tentative breakdown.
Differential Revision: https://reviews.llvm.org/D31997
llvm-svn: 300183
In getEntryCost(), make the scalar type for a compare instruction that of the
operands, not i1. This is needed in order to call getCmpSelInstrCost() for a
compare in a sensible way, the same way as the LoopVectorizer does.
New test: test/Transforms/SLPVectorizer/SystemZ/SLP-cmp-cost-query.ll
Review: Matthew Simpson
https://reviews.llvm.org/D31601
llvm-svn: 300061
The cost for a branch after vectorization is very different depending on if
the vectorizer will if-convert the block (branch is eliminated), or if
scalarized and predicated blocks will be produced (branch duplicated before
each block). There is also the case of remaining scalar branches, such as the
back-edge branch.
This patch handles these cases differently with TTI based cost estimates.
Review: Matthew Simpson
https://reviews.llvm.org/D31175
llvm-svn: 300058
Since SystemZ supports vector element load/store instructions, there is no
need for extracts/inserts if a vector load/store gets scalarized.
This patch lets Target specify that it supports such instructions by means of
a new TTI hook that defaults to false.
The use for this is in the LoopVectorizer getScalarizationOverhead() method,
which will with this patch produce a smaller sum for a vector load/store on
SystemZ.
New test: test/Transforms/LoopVectorize/SystemZ/load-store-scalarization-cost.ll
Review: Adam Nemet
https://reviews.llvm.org/D30680
llvm-svn: 300056
getArithmeticInstrCost(), getShuffleCost(), getCastInstrCost(),
getCmpSelInstrCost(), getVectorInstrCost(), getMemoryOpCost(),
getInterleavedMemoryOpCost() implemented.
Interleaved access vectorization enabled.
BasicTTIImpl::getCastInstrCost() improved to check for legal extending loads,
in which case the cost of the z/sext instruction becomes 0.
Review: Ulrich Weigand, Renato Golin.
https://reviews.llvm.org/D29631
llvm-svn: 300052
In the vectorization of first order recurrence, we vectorize such
that the last element in the vector will be the one extracted to pass into the
scalar remainder loop. However, this is not true when there is a phi (other
than the primary induction variable) is used outside the loop.
In such a case, we need the value from the second last iteration (i.e.
the phi value), not the last iteration (which would be the phi update).
I've added a test case for this. Also see PR32396.
A follow up patch would generate the correct code gen for such cases,
and turn this vectorization on.
Differential Revision: https://reviews.llvm.org/D31910
Reviewers: mssimpso
llvm-svn: 299985
This patch reapplies r298620. The original patch was reverted because of two
issues. First, the patch exposed a bug in InstCombine that caused the Chromium
builds to fail (PR32414). This issue was fixed in r299017. Second, the patch
introduced a bug in the vectorizer's scalars analysis that caused test suite
builds to fail on SystemZ. The scalars analysis was too aggressive and marked a
memory instruction scalar, even though it was going to be vectorized. This
issue has been fixed in the current patch and several new test cases for the
scalars analysis have been added.
llvm-svn: 299770
The vectorizer tries to replace truncations of induction variables with new
induction variables having the smaller type. After r295063, this optimization
was applied to all integer induction variables, including non-primary ones.
When optimizing the truncation of a non-primary induction variable, we still
need to transform the new induction so that it has the correct start value.
This should fix PR32419.
Reference: https://bugs.llvm.org/show_bug.cgi?id=32419
llvm-svn: 298882
Reason: breaks linking Chromium with LLD + ThinLTO (a pass crashes)
LLVM bug: https://bugs.llvm.org//show_bug.cgi?id=32413
Original change description:
[LV] Vectorize GEPs
This patch adds support for vectorizing GEPs. Previously, we only generated
vector GEPs on-demand when creating gather or scatter operations. All GEPs from
the original loop were scalarized by default, and if a pointer was to be stored
to memory, we would have to build up the pointer vector with insertelement
instructions.
With this patch, we will vectorize all GEPs that haven't already been marked
for scalarization.
The patch refines collectLoopScalars to more exactly identify the scalar GEPs.
The function now more closely resembles collectLoopUniforms. And the patch
moves vector GEP creation out of vectorizeMemoryInstruction and into the main
vectorization loop. The vector GEPs needed for gather and scatter operations
will have already been generated before vectoring the memory accesses.
Original Differential Revision: https://reviews.llvm.org/D30710
llvm-svn: 298735
This patch adds support for vectorizing GEPs. Previously, we only generated
vector GEPs on-demand when creating gather or scatter operations. All GEPs from
the original loop were scalarized by default, and if a pointer was to be stored
to memory, we would have to build up the pointer vector with insertelement
instructions.
With this patch, we will vectorize all GEPs that haven't already been marked
for scalarization.
The patch refines collectLoopScalars to more exactly identify the scalar GEPs.
The function now more closely resembles collectLoopUniforms. And the patch
moves vector GEP creation out of vectorizeMemoryInstruction and into the main
vectorization loop. The vector GEPs needed for gather and scatter operations
will have already been generated before vectoring the memory accesses.
Differential Revision: https://reviews.llvm.org/D30710
llvm-svn: 298620
The code for generating scalar base pointers in vectorizeMemoryInstruction is
not needed. We currently scalarize all GEPs and maintain the scalarized values
in VectorLoopValueMap. The GEP cloning in this unneeded code is the same as
that in scalarizeInstruction. The test cases that changed as a result of this
patch changed because we were able to reuse the scalarized GEP that we
previously generated instead of cloning a new one.
Differential Revision: https://reviews.llvm.org/D30587
llvm-svn: 298615
This patch refactors the PHisToFix loop as follows:
- The loop itself now resides in its own method.
- The new method iterates on scalar-loop's header; the PHIsToFix map formerly
propagated as an output parameter and filled during phi widening is removed.
- The code handling reductions is moved into its own method, similar to the
existing fixFirstOrderRecurrence().
Differential Revision: https://reviews.llvm.org/D30755
llvm-svn: 297740
Refactoring Cost Model's selectVectorizationFactor() so that it handles only the
selection of the best VF from a pre-computed range of candidate VF's, extracting
early-exit criteria and the computation of a MaxVF upper-bound to other methods,
all driven by a newly introduced LoopVectorizationPlanner.
Differential Revision: https://reviews.llvm.org/D30653
llvm-svn: 297737
getIntrinsicInstrCost() used to only compute scalarization cost based on types.
This patch improves this so that the actual arguments are checked when they are
available, in order to handle only unique non-constant operands.
Tests updates:
Analysis/CostModel/X86/arith-fp.ll
Transforms/LoopVectorize/AArch64/interleaved_cost.ll
Transforms/LoopVectorize/ARM/interleaved_cost.ll
The improvement in getOperandsScalarizationOverhead() to differentiate on
constants made it necessary to update the interleaved_cost.ll tests even
though they do not relate to intrinsics.
Review: Hal Finkel
https://reviews.llvm.org/D29540
llvm-svn: 297705
This commit is a follow-up on r297580. It fixes the FIXME added temporarily
by that commit to keep the removal of Unroller's specialized version of
scalarizeInstruction() an NFC. See https://reviews.llvm.org/D30715 for details.
llvm-svn: 297610
Unroller's specialized scalarizeInstruction() is mostly duplicating Vectorizer's
variant. OTOH Vectorizer's scalarizeInstruction() already supports the special
case of VF==1 except for avoiding mask-bit extraction in that case. This patch
removes Unroller's specialized version in favor of a unified method.
The only functional difference between the two variants seems to be setting
memcheck metadata for loads and stores only in Vectorizer's variant, which is a
bug in Unroller. To keep this patch an NFC the unified method doesn't set
memcheck metadata for VF==1.
Differential Revision: https://reviews.llvm.org/D30715
llvm-svn: 297580
This reverts r293386, r294027, r294029 and r296411.
Turns out the SLP tree isn't actually a "tree" and we don't handle
accessing the same packet of loads in several different orders well,
causing miscompiles.
Revert until we can fix this properly.
llvm-svn: 297493
Summary:
Similar to SmallPtrSet, this makes find and count work with both const
referneces and const pointers.
Reviewers: dblaikie
Subscribers: llvm-commits, mzolotukhin
Differential Revision: https://reviews.llvm.org/D30713
llvm-svn: 297424
Analyzing larger trees is extremely difficult with the current debug output so
this adds GraphTraits and DOTGraphTraits on top of the VectorizableTree data
structure. We can now display the SLP trees with Graphviz as in
https://reviews.llvm.org/F3132765.
I decorated the graph where a value needs to be gathered for one reason or
another. These are the red nodes.
There are other improvement I am planning to make as I work through my case
here. For example, I would also like to mark nodes that need to be extracted.
Differential Revision: https://reviews.llvm.org/D30731
llvm-svn: 297303
Because IRBuilder performs constant-folding, it's not guaranteed that an
instruction in the original loop map to an instruction in the vector loop. It
could map to a constant vector instead. The handling of first-order recurrences
was incorrectly making this assumption when setting the IRBuilder's insert
point.
llvm-svn: 297302
When expanding the set of uniform instructions beyond the seed instructions
(e.g., consecutive pointers), we mark a new instruction uniform if all its
loop-varying users are uniform. We should also allow users that are consecutive
or interleaved memory accesses. This fixes cases where we have an instruction
that is used as the pointer operand of a consecutive access but also used by a
non-memory instruction that later becomes uniform as part of the expansion.
llvm-svn: 297179
for VectorizeTree() API.This API uses it for proper mask computation to be used in shufflevector IR.
The fix is to compute the mask for out of order memory accesses while building the vectorizable tree
instead of actual vectorization of vectorizable tree.It also needs to recompute the proper Lane for
external use of vectorizable scalars based on shuffle mask.
Reviewers: mkuper
Differential Revision: https://reviews.llvm.org/D30159
Change-Id: Ide8773ce0ad3562f3cf4d1a0ad0f487e2f60ce5d
llvm-svn: 296863
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
llvm-svn: 296747
Summary:
The SLP vectorizer should propagate IR-level optimization hints/flags
(nsw, nuw, exact, fast-math) when converting scalar horizontal
reductions instructions into vectors, just like for other vectorized
instructions.
It doe not include IR propagation for extra arguments, we need to handle
original scalar operations for extra args to propagate correct flags.
Reviewers: mkuper, mzolotukhin, hfinkel
Subscribers: llvm-commits
Differential Revision: https://reviews.llvm.org/D30418
llvm-svn: 296614
Summary:
We should preserve IR flags for extra args. These IR flags should be
taken from original scalar operations, not from the reduction
operations.
Reviewers: mkuper, mzolotukhin, hfinkel
Subscribers: llvm-commits
Differential Revision: https://reviews.llvm.org/D30447
llvm-svn: 296613
Summary:
If horizontal reduction tree starts from the binary operation that is
used in PHI node, but this PHI is not used in horizontal reduction, we
may end up with extra addition of this PHI node after vectorization.
Here is an example:
```
%phi = phi i32 [ %tmp, %end], ...
...
%tmp = add i32 %tmp1, %tmp2
end:
```
after vectorization we always have something like:
```
%phi = phi i32 [ %tmp, %end], ...
...
%red = extractelement <8 x 32> %vec.red, 0
%tmp = add i32 %red, %phi
end:
```
even if `%phi` is not used in reduction tree. Patch considers these PHI
nodes as extra arguments and considers them in the final result iff they
really used in reduction.
Reviewers: mkuper, hfinkel, mzolotukhin
Subscribers: llvm-commits
Differential Revision: https://reviews.llvm.org/D30409
llvm-svn: 296606
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.
llvm-svn: 296578
for VectorizeTree() API.This API uses it for proper mask computation to be used in shufflevector IR.
The fix is to compute the mask for out of order memory accesses while building the vectorizable tree
instead of actual vectorization of vectorizable tree.
Reviewers: mkuper
Differential Revision: https://reviews.llvm.org/D30159
Change-Id: Id1e287f073fa4959713ba545fa4254db5da8b40d
llvm-svn: 296575
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
llvm-svn: 296145
result
Summary:
If the same value is used several times as an extra value, SLP
vectorizer takes it into account only once instead of actual number of
using.
For example:
```
int val = 1;
for (int y = 0; y < 8; y++) {
for (int x = 0; x < 8; x++) {
val = val + input[y * 8 + x] + 3;
}
}
```
We have 2 extra rguments: `1` - initial value of horizontal reduction
and `3`, which is added 8*8 times to the reduction. Before the patch we
added `1` to the reduction value and added once `3`, though it must be
added 64 times.
Reviewers: mkuper, mzolotukhin
Subscribers: llvm-commits
Differential Revision: https://reviews.llvm.org/D30262
llvm-svn: 295972
result
Summary:
If the same value is used several times as an extra value, SLP
vectorizer takes it into account only once instead of actual number of
using.
For example:
```
int val = 1;
for (int y = 0; y < 8; y++) {
for (int x = 0; x < 8; x++) {
val = val + input[y * 8 + x] + 3;
}
}
```
We have 2 extra rguments: `1` - initial value of horizontal reduction
and `3`, which is added 8*8 times to the reduction. Before the patch we
added `1` to the reduction value and added once `3`, though it must be
added 64 times.
Reviewers: mkuper, mzolotukhin
Subscribers: llvm-commits
Differential Revision: https://reviews.llvm.org/D30262
llvm-svn: 295956
result
Summary:
If the same value is used several times as an extra value, SLP
vectorizer takes it into account only once instead of actual number of
using.
For example:
```
int val = 1;
for (int y = 0; y < 8; y++) {
for (int x = 0; x < 8; x++) {
val = val + input[y * 8 + x] + 3;
}
}
```
We have 2 extra rguments: `1` - initial value of horizontal reduction
and `3`, which is added 8*8 times to the reduction. Before the patch we
added `1` to the reduction value and added once `3`, though it must be
added 64 times.
Reviewers: mkuper, mzolotukhin
Subscribers: llvm-commits
Differential Revision: https://reviews.llvm.org/D30262
llvm-svn: 295949
Implement isLegalToVectorizeLoadChain for AMDGPU to avoid
producing private address spaces accesses that will need to be
split up later. This was doing the wrong thing in the case
where the queried chain was an even number of elements.
A possible <4 x i32> store was being split into
store <2 x i32>
store i32
store i32
rather than
store <2 x i32>
store <2 x i32>
when legal.
llvm-svn: 295933
Summary:
If the same value is used several times as an extra value, SLP
vectorizer takes it into account only once instead of actual number of
using.
For example:
```
int val = 1;
for (int y = 0; y < 8; y++) {
for (int x = 0; x < 8; x++) {
val = val + input[y * 8 + x] + 3;
}
}
```
We have 2 extra rguments: `1` - initial value of horizontal reduction
and `3`, which is added 8*8 times to the reduction. Before the patch we
added `1` to the reduction value and added once `3`, though it must be
added 64 times.
Reviewers: mkuper, mzolotukhin
Subscribers: llvm-commits
Differential Revision: https://reviews.llvm.org/D30262
llvm-svn: 295868
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.
llvm-svn: 295858
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
llvm-svn: 295456
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
llvm-svn: 295063
back into a vector
Previously the cost of the existing ExtractElement/ExtractValue
instructions was considered as a dead cost only if it was detected that
they have only one use. But these instructions may be considered
dead also if users of the instructions are also going to be vectorized,
like:
```
%x0 = extractelement <2 x float> %x, i32 0
%x1 = extractelement <2 x float> %x, i32 1
%x0x0 = fmul float %x0, %x0
%x1x1 = fmul float %x1, %x1
%add = fadd float %x0x0, %x1x1
```
This can be transformed to
```
%1 = fmul <2 x float> %x, %x
%2 = extractelement <2 x float> %1, i32 0
%3 = extractelement <2 x float> %1, i32 1
%add = fadd float %2, %3
```
because though `%x0` and `%x1` have 2 users each other, these users are
part of the vectorized tree and we can consider these `extractelement`
instructions as dead.
Differential Revision: https://reviews.llvm.org/D29900
llvm-svn: 295056
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
llvm-svn: 295038
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.
llvm-svn: 294973
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
llvm-svn: 294967
reductions.
Currently, LLVM supports vectorization of horizontal reduction
instructions with initial value set to 0. Patch supports vectorization
of reduction with non-zero initial values. Also, it supports a
vectorization of instructions with some extra arguments, like:
```
float f(float x[], int a, int b) {
float p = a % b;
p += x[0] + 3;
for (int i = 1; i < 32; i++)
p += x[i];
return p;
}
```
Patch allows vectorization of this kind of horizontal reductions.
Differential Revision: https://reviews.llvm.org/D29727
llvm-svn: 294934
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
llvm-svn: 294782
We previously only created a vector phi node for an induction variable if its
type matched the type of the canonical induction variable.
Differential Revision: https://reviews.llvm.org/D29776
llvm-svn: 294755
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
llvm-svn: 294503
This breaks when one of the extra values is also a scalar that
participates in the same vectorization tree which we'll end up
reducing.
llvm-svn: 294245
Currently LLVM supports vectorization of horizontal reduction
instructions with initial value set to 0. Patch supports vectorization
of reduction with non-zero initial values. Also it supports a
vectorization of instructions with some extra arguments, like:
float f(float x[], int a, int b) {
float p = a % b;
p += x[0] + 3;
for (int i = 1; i < 32; i++)
p += x[i];
return p;
}
Patch allows vectorization of this kind of horizontal reductions.
Differential Revision: https://reviews.llvm.org/D28961
llvm-svn: 293994
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
llvm-svn: 293788
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
llvm-svn: 293459
The jumbled scalar loads will be sorted while building the tree and these accesses will be marked to generate shufflevector after the vectorized load with proper mask.
Reviewers: hfinkel, mssimpso, mkuper
Differential Revision: https://reviews.llvm.org/D26905
Change-Id: I9c0c8e6f91a00076a7ee1465440a3f6ae092f7ad
llvm-svn: 293386
Some checks in SLP horizontal reduction analysis function are performed
several times, though it is enough to perform these checks only once
during an initial attempt at adding candidate for the reduction
instruction/reduced value.
Differential Revision: https://reviews.llvm.org/D29175
llvm-svn: 293274
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.
llvm-svn: 293168
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
llvm-svn: 293155
instructions.
If number of instructions in horizontal reduction list is not power of 2
then only PowerOf2Floor(NumberOfInstructions) last elements are actually
vectorized, other instructions remain scalar. Patch tries to vectorize
the remaining elements either.
Differential Revision: https://reviews.llvm.org/D28959
llvm-svn: 293042
Removed data members ReduxWidth and MinVecRegSize + some C++11 stylish
improvements.
Differential Revision: https://reviews.llvm.org/D29010
llvm-svn: 292899
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
llvm-svn: 292456
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
llvm-svn: 292424
If a memory instruction will be vectorized, but it's pointer operand is
non-consecutive-like, the instruction is a gather or scatter operation. Its
pointer operand will be non-uniform. This should fix PR31671.
Reference: https://llvm.org/bugs/show_bug.cgi?id=31671
Differential Revision: https://reviews.llvm.org/D28819
llvm-svn: 292254
a function's CFG when that CFG is unchanged.
This allows transformation passes to simply claim they preserve the CFG
and analysis passes to check for the CFG being preserved to remove the
fanout of all analyses being listed in all passes.
I've gone through and removed or cleaned up as many of the comments
reminding us to do this as I could.
Differential Revision: https://reviews.llvm.org/D28627
llvm-svn: 292054
The removed assert seems bogus - it's perfectly legal for the roots of the
vectorized subtrees to be equal even if the original scalar values aren't,
if the original scalars happen to be equivalent.
This fixes PR31599.
Differential Revision: https://reviews.llvm.org/D28539
llvm-svn: 291692
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
llvm-svn: 291657
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
llvm-svn: 291651
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
llvm-svn: 291462
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
llvm-svn: 291106
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.
llvm-svn: 289975
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.
llvm-svn: 289958
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.
llvm-svn: 289934
This patch sets the default value of the "-enable-cond-stores-vec" command line
option to "true".
Differential Revision: https://reviews.llvm.org/D27814
llvm-svn: 289863
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...
llvm-svn: 289756
We currently check if the exact trip count is known and is smaller than the
"tiny loop" bound. We should be checking the maximum bound on the trip count
instead.
Differential Revision: https://reviews.llvm.org/D27690
llvm-svn: 289583
This patch ensures the correct minimum bit width during type-shrinking.
Previously when type-shrinking, we always sign-extended values back to their
original width. However, if we are going to sign-extend, and the sign bit is
unknown, we have to increase the minimum bit width by one bit so the
sign-extend will fill the upper bits correctly. If the sign bit is known to be
zero, we can perform a zero-extend instead. This should fix PR31243.
Reference: https://llvm.org/bugs/show_bug.cgi?id=31243
Differential Revision: https://reviews.llvm.org/D27466
llvm-svn: 289470
When trying to vectorize trees that start at insertelement instructions
function tryToVectorizeList() uses vectorization factor calculated as
MinVecRegSize/ScalarTypeSize. But sometimes it does not work as tree
cost for this fixed vectorization factor is too high.
Patch tries to improve the situation. It tries different vectorization
factors from max(PowerOf2Floor(NumberOfVectorizedValues),
MinVecRegSize/ScalarTypeSize) to MinVecRegSize/ScalarTypeSize and tries
to choose the best one.
Differential Revision: https://reviews.llvm.org/D27215
llvm-svn: 289043
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
llvm-svn: 288909
This reverts commit r288497, as it broke the AArch64 build of Compiler-RT's
builtins (twice: once in r288412 and once in r288497). We should investigate
this offline.
llvm-svn: 288508
When trying to vectorize trees that start at insertelement instructions
function tryToVectorizeList() uses vectorization factor calculated as
MinVecRegSize/ScalarTypeSize. But sometimes it does not work as tree
cost for this fixed vectorization factor is too high.
Patch tries to improve the situation. It tries different vectorization
factors from max(PowerOf2Floor(NumberOfVectorizedValues),
MinVecRegSize/ScalarTypeSize) to MinVecRegSize/ScalarTypeSize and tries
to choose the best one.
Differential Revision: https://reviews.llvm.org/D27215
llvm-svn: 288497
When trying to vectorize trees that start at insertelement instructions
function tryToVectorizeList() uses vectorization factor calculated as
MinVecRegSize/ScalarTypeSize. But sometimes it does not work as tree
cost for this fixed vectorization factor is too high.
Patch tries to improve the situation. It tries different vectorization
factors from max(PowerOf2Floor(NumberOfVectorizedValues),
MinVecRegSize/ScalarTypeSize) to MinVecRegSize/ScalarTypeSize and tries
to choose the best one.
Differential Revision: https://reviews.llvm.org/D27215
llvm-svn: 288412
Currently when cost of scalar operations is evaluated the vector type is
used for scalar operations. Patch fixes this issue and fixes evaluation
of the vector operations cost.
Several test showed that vector cost model is too optimistic. It
allowed vectorization of 8 or less add/fadd operations, though scalar
code is faster. Actually, only for 16 or more operations vector code
provides better performance.
Differential Revision: https://reviews.llvm.org/D26277
llvm-svn: 288398
Dehao Chen