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 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
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
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
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
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
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
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
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
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
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
Jonas Paulsson
Michael Kuperstein
Adam Nemet
Mohammad Shahid
Hans Wennborg
Alexey Bataev