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
Gil Rapaport