similar to: Compiling R with multi-threaded BLAS math libraries - why not actually ?

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

Hi, I have seen a lot of problems from people trying to compile R with MKL. So I am writing my experience in case it helps and to ask one question. I installed R-2.8.1.patched in Ubuntu 9.04 (gcc 4.3.3) using MKL 10.1.1.019. I configured correctly (following MKL userguide) with : sudo ./configure --with-blas="-I/opt/intel/mkl/10.1.1.019/include -L/opt/intel/mkl/10.1.1.019/lib/em64t

Hi, I have seen a lot of problems from people trying to compile R with MKL. So I am writing my experience in case it helps and to ask one question. I installed R-2.8.1.patched in Ubuntu 9.04 (gcc 4.3.3) using MKL 10.1.1.019. I configured correctly (following MKL userguide) with : sudo ./configure --with-blas="-I/opt/intel/mkl/10.1.1.019/include -L/opt/intel/mkl/10.1.1.019/lib/em64t

Dear R-devel: The Cluster administrators at KU got enthusiastic about testing R-3.2.2 with Intel MKL when I asked for some BLAS integration. Below I forward a performance report, which is encouraging, and thought you would like to know the numbers. Appears to my untrained eye there are some extraordinary speedups on Cholesky decomposition, determinants, and matrix inversion. They had

NO Hi. I'm not sure if this is an R-help or R-devel problem, so I'm starting here in the hope that someone can help (and willing to go to the other list if it's more appropriate). I think I am following all of the instructions in the various manuals, but clearly I am missing something. I have an Intel EM64T Dell with 2 dual-core Xeon processors running Red Hat EL5. I would

So I have two Dell power-edge machines running latest Ubuntu (13.04) with latest version of R (3.01). I installed revolution-mkl on one machine (T-410) and it works fine. On the second machine (T-620), it says the latest version of revolution-mkl is installed, but I'm getting no multi-processor functioning (R-benchmark-25.R) runs on a single processor and takes same amount of time

Hi! I've been playing with MKL for a few days and I noticed the instructions in the R Installation Administration manual [1] no longer apply. It seems that since version 10.0 (the one used by the manual), libmkl_lapack.so has been renamed/split (although the official explanations seem to imply this was already the case in 10.0 [2]). As a consequence, the instructions for dynamic linking no

Dear all, Given that the Intel MKL multithreaded BLAS is now free (https://software.intel.com/en-us/mkl/choose-download, released under the very permissive Intel Simplified Software License, https://software.intel.com/en-us/license/intel-simplified-software-license) and that this BLAS comes out as the fastest in most benchmarks, and that Microsoft R Open compiled against Intel MKL seems to be

I want to build R optimized, with either MKL, OpenBLAS or ATLAS. My OS: Fedora 22 Hardware: CPU op-mode(s): 32-bit, 64-bit Byte Order: Little Endian CPU(s): 8 Thread(s) per core: 2 Vendor ID: GenuineIntel Model name: Intel(R) Core(TM) i7-2600K CPU @ 3.40GHz I am a little confused when it comes to choose a method and would like to hear your experiences. If I am right, I have 3 possibilities: -

I've attempted to use the optimized GotoBLAS2 library with a freshly compiled R-2.11.1 patched on 64-bit Ubuntu 10.4. (See details below). When I run this version of R, the timings are not different than the original install, and only one core is used in test computations. Can someone point out my error? Thanks. --Dale Details ... I'm running the 64-bit version of Ubuntu 10.04 on my 2

Hi folks, if you want to use MKL (the fast BLAS I have tested on my Thinkpad T410) with the R 2.14.0 built as Debian/Ubuntu packages available on CRAN mirror, the following tricks may works for without some known side-effects (likes openmp breaking issues), you may try to build your own libblas.so.3gf.0 with following command: $ gfortran -L/opt/intel/lib/intel64 -liomp5

On Mon, Aug 24, 2015 at 11:29 AM, Bj?rn-Helge Mevik <b.h.mevik at usit.uio.no> wrote: > arnaud gaboury <arnaud.gaboury at gmail.com> writes: > >> - Intel MKL: this is part of Intel Parallel Studio and is a paid >> software. Now, there is the MKL package distributed by >> Revolutionanalytics, but I am not certain how this can be distributed >> for free. Is

On 13/06/2019 16:14, Dirk Eddelbuettel wrote: > On 13 June 2019 at 16:05, lejeczek via R-devel wrote: > | I'd like to ask, and I believe this place here should be best as who can > | know better, if building R with different compilers and opt flags is > | something worth investing time into? > | > | Or maybe this a subject that somebody has already investigated. If yes >

Hello How do you compare R to SAS in terms of speed and management of large datasets? What about Revolution R? I've seen on their site, they claim that Revolution R is much faster than R and it's multithread... Can you really notice the difference?. What dissadvantage does it have? I think it's based on R 2.10. but R already issued the version 2.12 Regards What alternative

Buenas, Alguno ha probado a usar Microsfot R Open? Segun leo, se han centrado en hacer que R por defecto sea multicore (cosa que podemos hacer usando parallel, foreach...) https://mran.revolutionanalytics.com/documents/rro/multithread Home [mran.revolutionanalytics.com]<https://mran.revolutionanalytics.com/documents/rro/multithread> This site uses cookies for analytics, personalized

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 I just read the thread on gotoBLAS, as well as the excellent vignette of gcbd. I still have some confusion and would like to ask very basic questions, hope I am not taking too much of your time. The point that retained my attention was the question of using implicit (multi-threaded blas) versus explicit (parallel code) optimisation. As I understood, the ideal would be to use a

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

I believe that actually means configure was able to use the BLAS you specified (MKL in this case). You can scan through config.log to make sure. Andy > From: > > Fedora Core 3 > > I installed the Intel MLk, > > and tried : --with-blas="-lmkl -lguide -lpthread" > > and got : External libraries: readline, BLAS(generic) > > thus I'm

As an end-user, I wonder about Revolution R. Is the relationship between Revolution R and the R community at-large a positive one? Do the former contribute to the development efforts of the latter? Is there a competitive aspect? is their forum competitive with r-help? any other thoughts? (most of all, I simply hope that they help some of the many helpful experts on this forum, who have