For faster R use OpenBLAS
From: http://www.stat.cmu.edu/~nmv/2013/07/09/for-faster-r-use-openblas-instead-better-than-atlas-trivial-to-switch-to-on-ubuntu/
Installing additional BLAS libraries on Ubuntu
For Ubuntu, there are currently three different BLAS options that can be easily chosen: "libblas" the reference BLAS, "libatlas" the ATLAS BLAS, and "libopenblas" the OpenBLAS. Their package names are
$ apt-cache search libblas libblas-dev - Basic Linear Algebra Subroutines 3, static library libblas-doc - Basic Linear Algebra Subroutines 3, documentation libblas3gf - Basic Linear Algebra Reference implementations, shared library libatlas-base-dev - Automatically Tuned Linear Algebra Software, generic static libatlas3gf-base - Automatically Tuned Linear Algebra Software, generic shared libblas-test - Basic Linear Algebra Subroutines 3, testing programs libopenblas-base - Optimized BLAS (linear algebra) library based on GotoBLAS2 libopenblas-dev - Optimized BLAS (linear algebra) library based on GotoBLAS2
Since libblas already comes with Ubuntu, we only need to install the other two for our tests. (NOTE: In the following command, delete 'libatlas3gf-base' if you don't want to experiment with ATLAS.):
$ sudo apt-get install libopenblas-base libatlas3gf-base
Switching between BLAS libraries
Now we can switch between the different BLAS options that are installed:
$ sudo update-alternatives --config libblas.so.3 There are 3 choices for the alternative libblas.so.3gf (providing /usr/lib/libblas.so.3gf). Selection Path Priority Status ------------------------------------------------------------ * 0 /usr/lib/openblas-base/libopenblas.so.0 40 auto mode 1 /usr/lib/atlas-base/atlas/libblas.so.3gf 35 manual mode 2 /usr/lib/libblas/libblas.so.3gf 10 manual mode 3 /usr/lib/openblas-base/libopenblas.so.0 40 manual mode Press enter to keep the current choice[*], or type selection number:Side note: If the above returned:
update-alternatives: error: no alternatives for libblas.so.3gf
Try
$ sudo update-alternatives --config libblas.so.3
instead. See the comments at the end of the post for further details.
From the selection menu, I picked 3, so it now shows that choice 3 (OpenBLAS) is selected:
$ sudo update-alternatives --config libblas.so.3gf There are 3 choices for the alternative libblas.so.3gf (providing /usr/lib/libblas.so.3gf). Selection Path Priority Status ------------------------------------------------------------ 0 /usr/lib/openblas-base/libopenblas.so.0 40 auto mode 1 /usr/lib/atlas-base/atlas/libblas.so.3gf 35 manual mode 2 /usr/lib/libblas/libblas.so.3gf 10 manual mode * 3 /usr/lib/openblas-base/libopenblas.so.0 40 manual mode
And we can pull the same trick to choose between LAPACK implementations. From the output we can see that OpenBLAS does not provide a new LAPACK implementation, but ATLAS does:
$ sudo update-alternatives --config liblapack.so.3 There are 2 choices for the alternative liblapack.so.3gf (providing /usr/lib/liblapack.so.3gf). Selection Path Priority Status ------------------------------------------------------------ * 0 /usr/lib/atlas-base/atlas/liblapack.so.3gf 35 auto mode 1 /usr/lib/atlas-base/atlas/liblapack.so.3gf 35 manual mode 2 /usr/lib/lapack/liblapack.so.3gf 10 manual mode
So we will do nothing in this case, since OpenBLAS is supposed to use the reference implementation (which is already selected).
Checking that R is using the right BLAS
Now we can check that everything is working by starting R in a new terminal:
$ R R version 3.0.1 (2013-05-16) -- "Good Sport" Copyright (C) 2013 The R Foundation for Statistical Computing Platform: x86_64-pc-linux-gnu (64-bit) ...snip... Type 'q()' to quit R. >
Great. Let's see if R is using the BLAS and LAPACK libraries we selected. To do so, we open another terminal so that we can run a few more shell commands. First, we find the PID of the R process we just started. Your output will look something like this:
$ ps aux | grep exec/R 1000 18065 0.4 1.0 200204 87568 pts/1 Sl+ 09:00 0:00 /usr/lib/R/bin/exec/R root 19250 0.0 0.0 9396 916 pts/0 S+ 09:03 0:00 grep --color=auto exec/R
The PID is the second number on the '/usr/lib/R/bin/exec/R' line. To see
which BLAS and LAPACK libraries are loaded with that R session, we use the "list open files" command:
$ lsof -p 18065 | grep 'blas\|lapack' R 18065 nathanvan mem REG 8,1 9342808 12857980 /usr/lib/lapack/liblapack.so.3gf.0 R 18065 nathanvan mem REG 8,1 19493200 13640678 /usr/lib/openblas-base/libopenblas.so.0
As expected, the R session is using the reference LAPACK (/usr/lib/lapack/liblapack.so.3gf.0) and OpenBLAS (/usr/lib/openblas-base/libopenblas.so.0)
Testing the different BLAS/LAPACK combinations
I used Simon Urbanek's most recent benchmark script. To follow along, first download it to your current working directory:
$ curl http://r.research.att.com/benchmarks/R-benchmark-25.R -O
And then run it:
$ cat R-benchmark-25.R | time R --slave Loading required package: Matrix Loading required package: lattice Loading required package: SuppDists Warning message: In library(package, lib.loc = lib.loc, character.only = TRUE, logical.return = TRUE, : there is no package called ‘SuppDists’ ...snip...
Ooops. I don't have the SuppDists package installed. I can easily load it via Michael Rutter's ubuntu PPA:
$ sudo apt-get install r-cran-suppdists
Now Simon's script works wonderfully. Full output
$ cat R-benchmark-25.R | time R --slave Loading required package: Matrix Loading required package: lattice Loading required package: SuppDists Warning messages: 1: In remove("a", "b") : object 'a' not found 2: In remove("a", "b") : object 'b' not found R Benchmark 2.5 =============== Number of times each test is run__________________________: 3 I. Matrix calculation --------------------- Creation, transp., deformation of a 2500x2500 matrix (sec): 1.36566666666667 2400x2400 normal distributed random matrix ^1000____ (sec): 0.959 Sorting of 7,000,000 random values__________________ (sec): 1.061 2800x2800 cross-product matrix (b = a' * a)_________ (sec): 1.777 Linear regr. over a 3000x3000 matrix (c = a \ b')___ (sec): 1.00866666666667 -------------------------------------------- Trimmed geom. mean (2 extremes eliminated): 1.13484335940626 II. Matrix functions -------------------- FFT over 2,400,000 random values____________________ (sec): 0.566999999999998 Eigenvalues of a 640x640 random matrix______________ (sec): 1.379 Determinant of a 2500x2500 random matrix____________ (sec): 1.69 Cholesky decomposition of a 3000x3000 matrix________ (sec): 1.51366666666667 Inverse of a 1600x1600 random matrix________________ (sec): 1.40766666666667 -------------------------------------------- Trimmed geom. mean (2 extremes eliminated): 1.43229160585452 III. Programmation ------------------ 3,500,000 Fibonacci numbers calculation (vector calc)(sec): 1.10533333333333 Creation of a 3000x3000 Hilbert matrix (matrix calc) (sec): 1.169 Grand common divisors of 400,000 pairs (recursion)__ (sec): 2.267 Creation of a 500x500 Toeplitz matrix (loops)_______ (sec): 1.213 Escoufier's method on a 45x45 matrix (mixed)________ (sec): 1.32600000000001 -------------------------------------------- Trimmed geom. mean (2 extremes eliminated): 1.23425893178325 Total time for all 15 tests_________________________ (sec): 19.809 Overall mean (sum of I, II and III trimmed means/3)_ (sec): 1.26122106386747 --- End of test --- 134.75user 16.06system 1:50.08elapsed 137%CPU (0avgtext+0avgdata 1949744maxresident)k 448inputs+0outputs (3major+1265968minor)pagefaults 0swaps
Where the elapsed time at the very bottom is the part that we care about. With OpenBLAS and the reference LAPACK, the script took 1 minute and 50 seconds to run. By changing around the selections with update-alternatives, we can test out R with ATLAS (3:21) or R with the reference BLAS (9:13). For my machine, OpenBLAS is a clear winner.