Hi, There is a major update on the benchmarks. I've performed them on a newer hardware with 2 CPUs and 3 GPUs. The latter can be used by NVBLAS for parallelizing matrix-matrix multiplication. Results are in the same spreadsheet as previously: https://docs.google.com/spreadsheets/d/1lWdVSuSragOobb0A_oeouQgHUMx378T9J5r7kwKSPkY/edit?usp=sharing Previous results are on the separate sheet of the same document. I have also created a github page with benchmark source code and few explanatory comments: https://github.com/avulanov/scala-blas
I was able to use all 3 GPUs for NVBLAS but BIDMat used only one GPU. John, could you suggest how to force BIDMat to use all GPUs? Also, could you suggest how to test Double matrices multiplication in BIDMat-cuda (in GPU and with copy from/to main memory)? Best regards, Alexander -----Original Message----- From: Ulanov, Alexander Sent: Wednesday, April 01, 2015 12:11 PM To: Xiangrui Meng; Sean Owen Cc: Evan R. Sparks; Sam Halliday; dev@spark.apache.org; jfcanny Subject: RE: Using CUDA within Spark / boosting linear algebra FYI, I've added instructions to Netlib-java wiki, Sam added the link to them from the project's readme.md https://github.com/fommil/netlib-java/wiki/NVBLAS Best regards, Alexander -----Original Message----- From: Xiangrui Meng [mailto:men...@gmail.com] Sent: Monday, March 30, 2015 2:43 PM To: Sean Owen Cc: Evan R. Sparks; Sam Halliday; dev@spark.apache.org; Ulanov, Alexander; jfcanny Subject: Re: Using CUDA within Spark / boosting linear algebra Hi Alex, Since it is non-trivial to make nvblas work with netlib-java, it would be great if you can send the instructions to netlib-java as part of the README. Hopefully we don't need to modify netlib-java code to use nvblas. Best, Xiangrui On Thu, Mar 26, 2015 at 9:54 AM, Sean Owen <so...@cloudera.com> wrote: > The license issue is with libgfortran, rather than OpenBLAS. > > (FWIW I am going through the motions to get OpenBLAS set up by default > on CDH in the near future, and the hard part is just handling > libgfortran.) > > On Thu, Mar 26, 2015 at 4:07 PM, Evan R. Sparks <evan.spa...@gmail.com> wrote: >> Alright Sam - you are the expert here. If the GPL issues are >> unavoidable, that's fine - what is the exact bit of code that is GPL? >> >> The suggestion to use OpenBLAS is not to say it's the best option, >> but that it's a *free, reasonable default* for many users - keep in >> mind the most common deployment for Spark/MLlib is on 64-bit linux on EC2[1]. >> Additionally, for many of the problems we're targeting, this >> reasonable default can provide a 1-2 orders of magnitude improvement >> in performance over the f2jblas implementation that netlib-java falls back >> on. > > --------------------------------------------------------------------- > To unsubscribe, e-mail: dev-unsubscr...@spark.apache.org For > additional commands, e-mail: dev-h...@spark.apache.org > B KKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKCB [ X ܚX KK[XZ[ ] ][ X ܚX P \ ˘\X K ܙ B ܈Y][ۘ[ [X[ K[XZ[ ] Z[ \ ˘\X K ܙ B
