For faster R use OpenBLAS instead: better than ATLAS, trivial to switch to on Ubuntu

R speeds up when the Basic Linear Algebra System (BLAS) it uses is well tuned. The reference BLAS that comes with R and Ubuntu isn’t very fast. On my machine, it takes 9 minutes to run a well known R benchmarking script. If I use ATLAS, an optimized BLAS that can be easily installed, the same script takes 3.5 minutes. If I use OpenBLAS, yet another optimized BLAS that is equally easy to install, the same script takes 2 minutes. That’s a pretty big improvement!

In this post, I’ll show you how to install ATLAS and OpenBLAS, demonstrate how you can switch between them, and let you pick which you would like to use based on benchmark results. Before we get started, one quick shout out to Felix Riedel: thanks for encouraging me to look at OpenBLAS instead of ATLAS in your comment on my previous post.

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.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

Press enter to keep the current choice[*], or type selection number:

I selected 3, so that 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.3gf
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.

Give it a shot yourself. If you find something different, let me know.