On Tue, Aug 14, 2018 at 9:41 AM Ravi Varadhan <ravi.varad...@jhu.edu> wrote: > > Does Microsoft open R come with pre-compiled BLAS that is optimized for > matrix computations?
Yes, see https://mran.microsoft.com/rro#intelmkl1 for details. --Ista > > Thanks, > Ravi > > -----Original Message----- > From: Ista Zahn <istaz...@gmail.com> > Sent: Monday, August 13, 2018 3:18 PM > To: Ravi Varadhan <ravi.varad...@jhu.edu> > Cc: r-help@r-project.org > Subject: Re: [R] Fast matrix multiplication > > > On Mon, Aug 13, 2018 at 2:41 PM Ravi Varadhan <ravi.varad...@jhu.edu> wrote: > > > > Hi Ista, > > Thank you for the response. I use Windows. Is there a pre-compiled > > version of openBLAS for windows that would make it easy for me to use it? > > Not sure. If you want an easy way I would use MRO. More info at > https://mran.microsoft.com/rro#intelmkl1 > > --Ista > > > Thanks, > > Ravi > > > > -----Original Message----- > > From: Ista Zahn <istaz...@gmail.com> > > Sent: Friday, August 10, 2018 12:20 PM > > To: Ravi Varadhan <ravi.varad...@jhu.edu> > > Cc: r-help@r-project.org > > Subject: Re: [R] Fast matrix multiplication > > > > > > Hi Ravi, > > > > You can achieve substantial speed up by using a faster BLAS (e.g., OpenBLAS > > or MKL), especially on systems with multiple CPUs. On my (6 year old, but 8 > > core) system your example takes 3.9 seconds with using the reference BLAS > > and only 0.9 seconds using OpenBLAS. > > > > Best, > > Ista > > On Fri, Aug 10, 2018 at 11:46 AM Ravi Varadhan <ravi.varad...@jhu.edu> > > wrote: > > > > > > Hi, > > > > > > I would like to compute: A %*% B %*% t(A) > > > > > > > > > > > > A is a mxn matrix and B is an nxn symmetric, positive-definite matrix, > > > where m is large relative to n (e.g., m=50,000 and n=100). > > > > > > > > > > > > Here is a sample code. > > > > > > > > > > > > M <- 10000 > > > > > > N <- 100 > > > > > > A <- matrix(rnorm(M*N), M, N) > > > > > > B <- crossprod(matrix(rnorm(N*N), N, N)) # creating a symmetric > > > positive-definite matrix > > > > > > > > > > > > # method 1 > > > > > > system.time(D <- A %*% B %*% t(A)) > > > > > > > > > > > > # I can obtain speedup by using a Cholesky decomposition of B > > > > > > # method 2 > > > > > > system.time({ > > > > > > C <- t(chol(B)) > > > > > > E <- tcrossprod(A%*%C) > > > > > > }) > > > > > > > > > > > > all.equal(D, E) > > > > > > > > > > > > I am wondering how to obtain more substantial speedup. Any suggestions > > > would be greatly appreciated. > > > > > > > > > > > > Thanks, > > > > > > Ravi > > > > > > > > > > > > [[alternative HTML version deleted]] > > > > > > ______________________________________________ > > > R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see > > > https://stat.ethz.ch/mailman/listinfo/r-help > > > PLEASE do read the posting guide > > > http://www.R-project.org/posting-guide.html > > > and provide commented, minimal, self-contained, reproducible code. > > > ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
