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]] ______________________________________________ [hidden email] 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. |
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 <[hidden email]> 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]] > > ______________________________________________ > [hidden email] 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. ______________________________________________ [hidden email] 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. |
In reply to this post by Ravi Varadhan-2
Yeah, you might not be able to go much faster here unless A has some
specialized structure that you can take advantage of (e.g., sparsity)? On 8/10/18, 11:22 AM, "Ravi Varadhan" <[hidden email]> 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]] > >______________________________________________ >[hidden email] 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. > ______________________________________________ [hidden email] 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. |
In reply to this post by Ravi Varadhan-2
Hi Ravi,
Like Ista, I have also had success in using R with OpenBLAS on our Linux compute cluster. This will involve installing R from source. If you'd like, I can send you the configure settings I used, as well as the environment settings used to control OpenBLAS multithreading. The CRAN site also has some good documentation of this. Note that I had issues using the latest version of OpenBLAS (0.3.x) with R 3.5.1. I'm not sure if you would have the same issues as me, but to be safe I would suggest using OpenBLAS 0.2.x instead. Peter [[alternative HTML version deleted]] ______________________________________________ [hidden email] 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. |
In reply to this post by Ista Zahn
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? Thanks, Ravi -----Original Message----- From: Ista Zahn <[hidden email]> Sent: Friday, August 10, 2018 12:20 PM To: Ravi Varadhan <[hidden email]> Cc: [hidden email] 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 <[hidden email]> 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]] > > ______________________________________________ > [hidden email] 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. ______________________________________________ [hidden email] 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. |
On Mon, Aug 13, 2018 at 2:41 PM Ravi Varadhan <[hidden email]> 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 <[hidden email]> > Sent: Friday, August 10, 2018 12:20 PM > To: Ravi Varadhan <[hidden email]> > Cc: [hidden email] > 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 <[hidden email]> 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]] > > > > ______________________________________________ > > [hidden email] 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. > ______________________________________________ [hidden email] 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. |
On Mon, Aug 13, 2018 at 12:18 PM Ista Zahn <[hidden email]> wrote:
> > On Mon, Aug 13, 2018 at 2:41 PM Ravi Varadhan <[hidden email]> 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 OpenBLAS is provided as a binary for Windows, see http://www.openblas.net/ . You may need to compile R from source though, unless you can use an equivalent of the linux trick to replace libRblas.so with a symlink to the compiled openBLAS library. Peter ______________________________________________ [hidden email] 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. |
In reply to this post by Ista Zahn
Does Microsoft open R come with pre-compiled BLAS that is optimized for matrix computations?
Thanks, Ravi -----Original Message----- From: Ista Zahn <[hidden email]> Sent: Monday, August 13, 2018 3:18 PM To: Ravi Varadhan <[hidden email]> Cc: [hidden email] Subject: Re: [R] Fast matrix multiplication On Mon, Aug 13, 2018 at 2:41 PM Ravi Varadhan <[hidden email]> 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 <[hidden email]> > Sent: Friday, August 10, 2018 12:20 PM > To: Ravi Varadhan <[hidden email]> > Cc: [hidden email] > 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 <[hidden email]> 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]] > > > > ______________________________________________ > > [hidden email] 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. > ______________________________________________ [hidden email] 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. |
On Tue, Aug 14, 2018 at 9:41 AM Ravi Varadhan <[hidden email]> 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 <[hidden email]> > Sent: Monday, August 13, 2018 3:18 PM > To: Ravi Varadhan <[hidden email]> > Cc: [hidden email] > Subject: Re: [R] Fast matrix multiplication > > > On Mon, Aug 13, 2018 at 2:41 PM Ravi Varadhan <[hidden email]> 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 <[hidden email]> > > Sent: Friday, August 10, 2018 12:20 PM > > To: Ravi Varadhan <[hidden email]> > > Cc: [hidden email] > > 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 <[hidden email]> 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]] > > > > > > ______________________________________________ > > > [hidden email] 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. > > > ______________________________________________ [hidden email] 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. |
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