similar to: Vectorization width not correct using #pragma clang loop vectorize_width

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

Am Mi., 2. Okt. 2019 um 07:08 Uhr schrieb Florian Hahn via llvm-dev <llvm-dev at lists.llvm.org>: > The other thing is that with the patch behaviour is slightly changed and we could get a diagnostic we didn't get before: > > warning: loop not vectorized: the optimizer was unable to > perform the requested transformation; the transformation might be disabled or >

Hi Michael and Florian, ( + llvm-dev for visibility) I would like to quickly follow up on "Pragma vectorize_width() implies vectorize(enable)", which got reverted with commit 858a1ae for 2 reasons, see also that revert commit message. Ignore the assert, that's been fixed now. The other thing is that with the patch behaviour is slightly changed and we could get a diagnostic we

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]; } }

Hi -- I have found a bug in an HPC code where llvm is vectorizing a loop on Skylake that has an obvious recurrence. I derived a small test case based on the original benchmark below: /*****************************************************************/ static void __attribute__ ((always_inline)) one( const int *restrict in, const int *const end, const unsigned shift, int *const restrict index,

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

I'm trying to fit a nested mixed model using lmer and have some questions about the output and my model formulations. I have replicate measures on Lines which are strictly nested within Populations. (a) So if I want to fit a model where Line is a random effect and Populations are fixed and the random Line effect is constant across Populations, I have: measure_ijk = mu + P_i + L_ij +

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).

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, this is my first time using the nlme package, and I ran into the following puzzling problem. I estimated a mixed effects model using lme, once using groupedData, once explicitly stating the equations. I had the following outputs. All the coefficients were similar, but they're always slightly different, making me think that it's not due to numerical error. Also, what is the

> 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 Daniel, I increased the size of your test to be 128 but -stats still shows no loop optimized... Xiaochu On Aug 12, 2016 11:11 AM, "Daniel Berlin" <dberlin at dberlin.org> wrote: > It's not possible to know that A and B don't alias in this example. It's > almost certainly not profitable to add a runtime check given the size of > the loop. > > >

I'm not compiling it to x86. Should loop optimizer something independent of the target? If so, should the vectorized code on IR level? On Aug 12, 2016 11:39 AM, "Daniel Berlin" <dberlin at dberlin.org> wrote: > cat > test.c > > #define SIZE 128 > > void bar(int *restrict A, int* restrict B,int K) { > > #pragma clang loop vectorize(enable)

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

Hi Alex, This has been very recently fixed by Hal. See http://reviews.llvm.org/rL270771 Adam > On Jun 4, 2016, at 3:13 AM, Alex Susu via llvm-dev <llvm-dev at lists.llvm.org> wrote: > > Hello. > Mikhail, I come back to this older thread. > I need to do a few changes to LoopVectorize.cpp. > > One of them is related to figuring out the exact C source line

Hi Alex, I think the changes you want are actually not vectorizer related. Vectorizer just uses data provided by other passes. What you probably might want is to look into routine Loop::getStartLoc() (see lib/Analysis/LoopInfo.cpp). If you find a way to improve it, patches are welcome:) Thanks, Michael > On Jun 3, 2016, at 6:13 PM, Alex Susu <alex.e.susu at gmail.com> wrote: >

> 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 will start out with the caveat that I'm not a statistician by training, but have a fairly decent understanding of probability and likelihood. Nevertheless, I'm trying to fit a nonlinear model to a dataset which has two main factors using nlme. Within the dataset there are two Type categories and four Tissue categories, thus giving me 8 datasets in total. The dataset is in

Adam, thanks for all the suggestions! One nice aspect of the `-Rpass` family of options is that I can filter based on what I want. If I only want to see which inlines I missed, I could use `clang -Rpass-missed="inline"`, for example. On the other hand, optimization remark YAML always include remarks from all passes (as far as I can tell), which increases the amount of time it takes