search for: vectorizing

The following series adds initial vector support for PowerPC64. On POWER9, flac --best is about 3.3x faster. Amitay Isaacs (2): Add m4 macro to check for C __attribute__ features Check if compiler supports target attribute on ppc64 Anton Blanchard (5): configure.ac: Remove SPE detection code configure.ac: Add VSX enable/disable configure.ac: Fix FLAC__CPU_PPC on little endian, and add

RISC-V is an open and free instruction set architecture (ISA) used in numerous domains in industry and research. The vector extension (short: 'V') supplements the basic ISA with support for data parallel computations. This RFC sketches a strategy for targeting this instruction set extension in LLVM. Some but not all of what is proposed here has already been implemented out of tree. It is

Hello, I'm new on this list. First of all, thank you to all the developers of this great project! At the moment there is only an application that keeps me on both macos and windows, its name is PWGL a free environment for computer assisted composition in openGL. (http://www2.siba.fi/PWGL/) I'm running Ubuntu 8.04 and wine 0.9.59. I have to say that I also installed vcrun2005 and

I'm just going to add Kristof here since ARM is looking to add SVE here and this overlaps quite a bit with their goals. -eric On Wed, Apr 11, 2018 at 2:45 AM Robin Kruppe via llvm-dev < llvm-dev at lists.llvm.org> wrote: > RISC-V is an open and free instruction set architecture (ISA) used in > numerous domains in industry and research. The vector extension (short: >

On 13 April 2018 at 16:52, Robin Kruppe <robin.kruppe at gmail.com> wrote: > On 13 April 2018 at 14:37, Graham Hunter <Graham.Hunter at arm.com> wrote: > >> Hi, >> >> Nice to see another group tackling length agnostic vectorization :) >> >> I'm still reading through all the details, but I do have one initial >> question related to the

As some of you may know, I committed my basic-block autovectorization pass a few days ago. I encourage anyone interested to try it out (pass -vectorize to opt or -mllvm -vectorize to clang) and provide feedback. Especially in combination with -unroll-allow-partial, I have observed some significant benchmark speedups, but, I have also observed some significant slowdowns. I would like to share my

Hello. I am trying to vectorize a CSR SpMV (sparse matrix vector multiplication) procedure but the LLVM loop vectorizer is not able to handle such code. I am using cland and llvm version 3.4 (on Ubuntu 12.10). I use the -fvectorize option with clang and -loop-vectorize with opt-3.4 . The CSR SpMV function is inspired from

On 5/28/19 3:31 PM, Philip Reames via cfe-dev wrote: > I generally like the idea of having support in IR for vectorization of > custom functions.  I have several use cases which would benefit from this. > > I'd suggest a couple of reframings to the IR representation though. > > First, this should probably be specified as metadata/attribute on a > function declaration. 

Dear all, This RFC is a proposal to provide auto-vectorization functionality for user provided vector functions. The proposal is a modification of an RFC that I have sent out a couple of months ago, with the title `[RFC] Re-implementing -fveclib with OpenMP` (see http://lists.llvm.org/pipermail/llvm-dev/2018-December/128426.html). The previous RFC is to be considered abandoned. The original RFC

Hi, Comments below On 13 April 2018 at 16:52, Robin Kruppe <robin.kruppe at gmail.com<mailto:robin.kruppe at gmail.com>> wrote: On 13 April 2018 at 14:37, Graham Hunter <Graham.Hunter at arm.com<mailto:Graham.Hunter at arm.com>> wrote: Hi, Nice to see another group tackling length agnostic vectorization :) I'm still reading through all the details, but I do have one

Is there some (much) more efficient way to do this? VECTOR=c(3,2,4,5,5,3,3,5,1,6,6); NEWVECTOR=VECTOR[1]; for(i in 1:(length(VECTOR)-1)) { if((identical(VECTOR[i], VECTOR[i+1]))==FALSE){ NEWVECTOR=c(NEWVECTOR,VECTOR[i+1])} } > VECTOR [1] 3 2 4 5 5 3 3 5 1 6 6 > NEWVECTOR [1] 3 2 4 5 3 5 1 6 _______________________________ Atte Tenkanen University of Turku, Finland

...n candidates - including both outer and inner loops, and their respective VF/UF's. The current Loop Vectorizer works in the following three main steps: 1. Legal Step: check if loop can be legally vectorized; encode constraints and artifacts if so. 2. Cost Step: compute the relative cost of vectorizing it along possible vectorization and unroll factors. 3. Transform Step: vectorize the loop according to best vectorization and unroll factors. This design has some implications: 1. Cost Step tries to predict what the vectorized loop will look like and how much it will cost, independently of wha...

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 associated clauses based on the VectorABI [2]. On the caller side, we propose to improve LLVM loopVectorizer such that the code that calls the SIMD-enabled function can be vectoriz...

Hi, I'm trying to build llvm (git monorepo) on Ubuntu 18.04 with EXPENSIVE_CHECKS enabled and running into various errors compiling SourceMgr.cpp, depending on which host compiler I use. For example with GCC: $ CC=gcc-8 CXX=g++-8 cmake -GNinja -DCMAKE_BUILD_TYPE=Debug -DLLVM_ENABLE_EXPENSIVE_CHECKS=ON ~/git/llvm-project/llvm/ && ninja ... [89/2690] Building CXX object

Hello. Mikhail, with the more recent version of the LoopVectorize.cpp code (retrieved at the beginning of July 2016) I ran the following piece of C code: void foo(long *A, long *B, long *C, long N) { for (long i = 0; i < N; ++i) { C[i] = A[i] + B[i]; } } The vectorized LLVM program I obtain contains 2 vector.body blocks - one named

On 5/29/19 1:52 PM, Philip Reames wrote: > On 5/28/19 7:55 PM, Finkel, Hal J. wrote: >> On 5/28/19 3:31 PM, Philip Reames via cfe-dev wrote: >>> I generally like the idea of having support in IR for vectorization of >>> custom functions.  I have several use cases which would benefit from this. >>> >>> I'd suggest a couple of reframings to the IR

Hmm. What about the errors I quoted from using clang-7 (starting about a third of the way down my email, sorry if they got kinda lost in all the noise)? Thanks, Jay. On Tue, 3 Sep 2019 at 20:00, David Blaikie <dblaikie at gmail.com> wrote: > > Looks to me like a bug in GCC's constexpr+_GLIBCXX_CONCEPT_CHECKS support. Small test case: > > $ g++-8 test.cpp -std=c++2a

...h of 2 (the threshold is 6). If you give clang -mllvm -bb-vectorize-req-chain-depth=2 then it will vectorize. The reason the heuristic has such a large default value is to prevent cases where it costs more to permute all of the necessary values into and out of the vector registers than is saved by vectorizing. Does the code generated with -bb-vectorize-req-chain-depth=2 run faster than the unvectorized code? The heuristic can certainly be improved, and these kinds of test cases are very important to that improvement process. -Hal On Thu, 2012-02-09 at 13:27 +0100, Carl-Philip Hänsch wrote: > I ha...

...l 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 associated clauses based on the > VectorABI [2]. On the caller side, we propose to improve LLVM > loopVectorizer such that the code that calls the SIMD-enabled functio...

Hello, Is it intended that is.vector(...) and is(..., "vector") do not always give identical results? is.vector() works as documented ('is.vector' returns 'FALSE' if 'x' has any attributes except names.) Thus, A <- array(1:2, 1:2) M <- diag(2) is.vector(M) # FALSE, as documented # and is.vector(A) # FALSE, as documented # however is(M,