similar to: R and Multi threading

Hello all, I came across<http://www.r-bloggers.com/performance-benefits-of-linking-r-to-multithreaded-math-libraries/> David Smith's new post Performance benefits of linking R to multithreaded math libraries<http://blog.revolutionanalytics.com/2010/06/performance-benefits-of-multithreaded-r.html> Which explains how (and why) REvolution distribution of R uses different BLAS math

R 3.5.1 is installed under Linux Mint 19 from binaries r-base and r-base-dev from ubuntu bionic-cran35.? Mysteriously, packages using libRblas cannot be compiled.? I get: libRblas.so: cannot open shared object file: No such file or directory.? And indeed, I find no libRblas.so. Any ideas about how to fix this? Thanks, Steve

Hi all, I've compiled R (version 2.8.1) from the source code with "--with- blas=" option in order to use external multi-threaded blas package. However, it's very hard to verify that compilation is correct and R is using multi-threads correctly or not. Which command or operation in R will run with multi-threads? I assumed that matrix multiplication %*% may call multi-threaded

I'm using a multithreaded BLAS library with R and I see the expected speed improvements with matrix multiplication, svd, etc. However, glmnet continues to use only a single CPU. Since this package is compiled from Fortran, is this the expected behavior or is there a way to compile the glmnet package so that it uses the multithreaded BLAS library? Thank you Brian

Is there a way to use multi-threading with ffmpeg2theora? Pretty much any type i'm ok with, including multi-threaded theora encoding or multi-threaded ffmpeg decoding, or using half of my cpu cores on ffmpeg decoding and the other half on theora encoding. Is there a way to do multithreaded thoera encoding? -------------- next part -------------- An HTML attachment was scrubbed... URL:

I have been timing a particular model fit using lmer on several different computers and came up with a peculiar result - the model fit is considerably slower on a dual-core Athlon 64 using Goto's multithreaded BLAS than on a single-core processor. Here is the timing on a single-core Athlon 64 3000+ running under today's R-devel with version 0.995-5 of the Matrix package. >

Dear expeRts! I'm thinking of buying a new computer and am considering dual-core processors, such as AMD Athlon64 X2. Since I'm not a computer expert, pleas forgive me if some of my questions are silly. First, am I correct that using a dual-core processor is (for R point of view) the same as using a computer with two processors? If that is true, the posts I found on the list imply that

Hi all, I have a problem with cpu affinity in my R-2.11.1 installation compiled against ATLAS running on a Linux (Ubuntu 10.04) cluster under GridEngine. I wish to use Grid Engine's core binding feature to bind user processes into the number of cores they request on the cluster, thus preventing badly behaved multi-threaded libraries from consuming more cores than requested. An example of

Rhelpers: I recently installed the 64-bit version of R on my Debian system, and afterwards was asked if it was compiled using ATLAS. Is there a way to test to see if R is using ATLAS? --j

I agree that the "R Installation and Administration" guide statement about FlexiBLAS needs an update. Standard R in CentOS Stream 9, which is now downstream from Fedora, gives the following result for Matrix products in sessionInfo(): > sessionInfo() R version 4.3.1 (2023-06-16) Platform: x86_64-redhat-linux-gnu (64-bit) Running under: CentOS Stream 9 Matrix products: default

Hi Tomas, Thanks for your answer. Indeed, I checked and my R-3.4.1 installed from the ubuntu repository use 'libopenblasp-r0.2.18.so' while my R-3.3.2 that I did compiled on my machine use 'libRblas.so' which explain the difference of behavior. I will use RhpcBLASctl to avoid issue when combining matrix product and other multi-threading package. Maybe this point regarding

Dear R Core Team, I wish to report what can be viewed as a bug or at least a strange behavior in R-3.4.1. I ask my question here (as recommended on https://www.r-project.org/bugs.html) since I am not member of the R's Bugzilla. When running 'R --vanilla' from the command line, the standard matrix product is by default based on BLAS and multi-threaded on all cores available on

Hi Ista, Maybe a little comment in the 'matmult {base}' doc page or on the 'options {base}' in the field 'matprod' would be useful to remind users to be cautious regarding BLAS multi-threading? I understand why this is a BLAS related issue and not directly an R related issue. Nonetheless, my concern was for non-advanced R users, that may don't even know what BLAS

Hi, I was wondering if it is safe to call R functions and/or R BLAS functions from within multithreaded C/C++ code(.Call interface)? It is not in case with MATLAB. I was experimenting using pthreads and OpenMP. Thanks, Rob [[alternative HTML version deleted]]

Hi, everybody. I notice debian packages for Ubuntu on CRAN are missing "libRblas.so" and I'm trying to find out why. Why do I care? We are working in an hpc project in a CentOS Linux cluster. That led me to the question of "which BLAS is fastest?" Dirk E referred me this morning to the R admin manual. The standard R compilation creates a shared blas library libRblas.so

I was surprised, given the vector-ish nature of R, to see that (according to my CPU meters) there doesn't seem to be any obvious multithreading in R. Are there any plans to change this?

Dear list, is R capable of multithreading? At the moment it uses only 1 CPU out of 4 CPUs on my system. Is there a compilation option or start option so that R uses multiple CPUs at the same time to accomplish a calculation? Some information on my system: > R.version _ platform x86_64-pc-linux-gnu arch x86_64 os linux-gnu system x86_64,

Greetings All, For the last few months since the GCC Cauldron I've been researching/helping out plan for multi-threading GCC. I've posted my GCC ideas here: https://docs.google.com/document/d/1po_RRgSCtRyYgMHjV0itW8iOzJXpTdHYIpC9gUMjOxk/edit as I've heard there is interest on the LLVM side as well. I've talked to some of the IBM folks from the Toronto area about it face to face

Hello! I need to purchase a new box, which I would like to optimize for good R performance. For the record, I will run Fedora Core 5 as and OS, and I wanted to know if anyone has experience with how the following affects R performance: - Is there a big advantage to having a 64-bit CPU over having a 32-bit? - Does an Opteron offer any advantages over an Athlon, and if yes, does it justify an

Well, CentOS 5.3, which amounts to the same thing. I recently decided to upgrade my main research machine from Fedora Core 8 -> CentOS 5.3. Basically, I was looking to move to a distro with longer 'term-of-life' than the release schedule for Fedora currently allows. The machine is a multi-Opteron box, so both 32- and 64-bit apps natively supported. Since I do a lot of 'linear