similar to: llvm is illegally vectorizing with a recurrence on skylake

Hi, I am trying to compare the loop vectorizers effectiveness for different targets relative to each other. That way, I am hoping to find loops that are not vectorized - but could be - on my target by finding other targets doing this successfully. With some luck, there might be something in the Target files that could be fixed with improved vectorization as a result... I would like to do

Hi, I'm new to llvm and am trying to run benchmarks from the test-suite using lnt to check loop-vectorization for various benchmarks. Test are compiling and executing fine, but I am not getting optimization remarks while using flags like -Rpass-missed=loop-vectorize and -Rpass-analysis=loop-vectorize I've tried running it like this: lnt runtest test-suite --sandbox SANDBOX --cc

Hello, I'm learning how LLVM performs PGO (profile-guided optimizations) by using the instrumentation-based profile build (-fprofile-instr-generate and -fprofile-instr-use). However, I found there is no difference in inlining behaviors between with and without PGO for a few spec benchmarks by checking the emit optimization reports (-Rpass=inline -Rpass-missed=inline -Rpass-analysis=inline).

For -Rpass, and other related uses, I am looking at enabling column info by default. David pointed me at PR 14106, which seems to be the original motivation for introducing -gcolumn-info. However, I am finding no differences when using it on this test. I've tried building with/without -gcolumn-info and found almost no difference in compile time (+0.4%): $ /usr/bin/time clang -w -fno-builtin

Reduction Not Captured By LLVM CODE_1 ------------------------------------------------------------ ------------------------------------------------------------ -------------------- #include <stdio.h> int main() { int sum[1000]={1,2,3,4}; for (int i=1;i<1000;i++) { sum[0] +=sum[i-1]; } }

Dear community, Our team at IITH have been experimenting with loop-distribution pass in LLVM. We see the following results on few benchmarks. clang -O3 -mllvm -enable-loop-distribute -Rpass=loop-distribute file.c clang -O3 -mllvm -enable-loop-distribute -Rpass-analysis=loop-distribute file.c TORCH

Hello All, Searched around, haven't found a decent solution. I'd like to translate a vector of numbers to a matrix (or to a list of vectors) such that the vector values would serve as indicies of the 1's in an otherwise-zero-filled matrix (or list of vectors). For example: > a = c(1,3,3,4) # perform operation [,1] [,2] [,3] [,4] [1,] 1 0 0 0 [2,] 0 0 1

> On May 11, 2016, at 3:37 AM, Hal Finkel <hfinkel at anl.gov> wrote: > > ----- Original Message ----- >> From: "Adam Nemet" <anemet at apple.com> >> To: "Hal Finkel" <hfinkel at anl.gov> >> Cc: "llvm-dev (llvm-dev at lists.llvm.org)" <llvm-dev at lists.llvm.org> >> Sent: Wednesday, May 11, 2016 1:15:42 AM

Hello, I m trying to set vector width using #pragma clang loop vectorize_width(32) but i m getting width 8 for the following kernel; #define M 128 #define N 128 #define SQRT_FUN(x) sqrtf(x) int main(int argc, char** argv) { /* Variable declaration/allocation. */ double float_n = (double)N; double data[N*M]; double corr[M*M]; double mean[M]; double stddev[M]; uint32_t

Hi llvm-dev! We are doing some fuzzy testing using C program generators, and one question that came up when generating a program with both floating point arithmetic and loop pragmas was; Is the loop vectorizer really allowed to vectorize a loop when it can't prove that it is safe to reorder fp math, even if there is a loop pragma that hints about a preferred width. When reading here

This idea came up a few times recently [1][2] so I’d like start prototyping it. To summarize, we can emit optimization remarks using the -Rpass* options. These are currently emitted by optimizations like vectorization[3], unrolling, inlining and since last week loop distribution. For large programs however this can amount to a lot of diagnostics output to sift through. Filtering this by the

I've tried to do: clang++ -O3 -march=native -mtune=native \ -Rpass=loop-vectorize,slp-vectorize -Rpass-missed=loop-vectorize,slp-vectorize -Rpass-analysis=loop-vectorize,slp-vectorize \ -ffast-math -ffp-model=fast -ffp-exception-behavior=ignore -ffp-contract=fast \ -c -o vec.o vec.cc But I've got no feedback. -- Alexandre Bique

-----BEGIN PGP SIGNED MESSAGE----- Hash: SHA1 I have two matrices: > m1 <- matrix(1,4,4) > m1 [,1] [,2] [,3] [,4] [1,] 1 1 1 1 [2,] 1 1 1 1 [3,] 1 1 1 1 [4,] 1 1 1 1 > m2 <- matrix(0,3,3) > diag(m2) <- 1 > m2 [,1] [,2] [,3] [1,] 1 0 0 [2,] 0 1 0 [3,] 0 0 1 I want to get indicies from m2

% clang -S -O3 -mcpu=cortex-a57 -ffast-math -Rpass-analysis=loop-vectorize dot.c dot.c:15:1: remark: loop not vectorized: value that could not be identified as reduction is used outside the loop [-Rpass-analysis=loop-vectorize] } ^ dot.c:15:1: note: could not determine the original source location for :0:0 I found “llvm-as < /dev/null | llc -march=aarch64 -mattr=help” which listed a

Hi, I'm using clang frontend. I'm interested in some particular hot loop in my code and I emit a report from vectorizer optimizations passes. I receive nice output if passing -Rpass* flags as long as I'm building without LTO? But with -flto it just prints nothing. Is there a way to emit opt reports when using LTO? For now I can only approximate about whether my the loop will be

Hi Hal, > On May 10, 2016, at 5:39 PM, Hal Finkel <hfinkel at anl.gov> wrote: > > Hi Adam, > > I think would be a really useful feature to have. I don't think that the backend should be responsible for filtering, but should pass the relative hotness information to the frontend. Given that these diagnostics are not just going to be used for -Rpass and friends, but also

Proposal for function vectorization and loop vectorization with function calls ============================================================================== Intel Corporation (3/2/2016) This is a proposal for an initial work towards Clang and LLVM implementation of vectorizing a function annotated with OpenMP 4.5's "#pragma omp declare simd" (named SIMD-enabled function) and its

> On Oct 10, 2016, at 2:50 PM, Hal Finkel <hfinkel at anl.gov> wrote: > > > From: "Dangeti Tharun kumar via llvm-dev" <llvm-dev at lists.llvm.org <mailto:llvm-dev at lists.llvm.org>> > To: llvm-dev at lists.llvm.org <mailto:llvm-dev at lists.llvm.org> > Cc: "Santanu Das" <cs15mtech11018 at iith.ac.in <mailto:cs15mtech11018 at

Hi Quentin, You are correct, I could manage to get clang to use vrcpps, but not in a satisfying way: clang++ -O3 -march=native -mtune=native \ -Rpass=loop-vectorize -Rpass-missed=loop-vectorize -Rpass-analysis=loop-vectorize \ -ffast-math -ffp-model=fast -ffp-exception-behavior=ignore -ffp-contract=fast \ -c -o vec.o vec.cc 0000000000000140 <_Z4fct4Dv4_f>: 140: c5 f8 53 c8

Hi Alex, I'm not aware of efforts on loop coalescing in LLVM, but probably polly can do something like this. Also, one related thought: it might be worth making it a separate pass, not a part of loop vectorizer. LLVM already has several 'utility' passes (e.g. loop rotation), which primarily aims at enabling other passes. Thanks, Michael > On Feb 15, 2016, at 6:44 AM, RCU