Can we make our analysis using the R statistical computing and analysis platform run faster? Usually the answer is yes, and the best way is to improve your algorithm and variable selection.
But recently David Smith was suggesting that a big benefit of their (commercial) version of R was that it was linked to a to a better linear algebra library. So I decided to investigate.
The quick summary is that it only really makes a difference for fairly artificial benchmark tests. For “normal” work you are unlikely to see a difference most of the time.
The environment
I use R on a 64bit Fedora 12 Linux system. Fortunately, it is very easy to rebuild R using different libraries on this platform. For the following, I will assume that you have a working rpmbuild environment. The test system has a quad core Intel Xeon E5420 CPU with each core running at 2.50 GHz.
Benchmarks
Benchmarking R is complex. Very complex. But for this simple test we use two tests from the R Benchmarks page: MASSex.R and Rbenchmark25.R. The first is a simple benchmark using the examples from the MASS package, and has the advantage that it reflects realworld problems and realworld analysis, albeit small problems and short analysis. The second is a much more artificial example and primarily test matrix operations.
We run the MASS benchmark as:
/usr/bin/time p R vanilla CMD BATCH MASSex.R /dev/null
While the Rbenchmark25 is simply:
Rscript vanilla Rbenchmark25.R
For the MASS benchmark we simply capture the real elapsed time while the R benchmark 2.5 provides more detailed output for the three classes of tests (matrix calculation, functions, and program execution) as well as overall summaries. They are all shown in the table below.
Compileroptimized R
For the experiments that follow the first thing to do is to grab copies of the source RPMs for R and for ATLAS:
cd ~/rpmbuild/SRPMS yumdownloader source atlas R cd ..
At the time I did this, I got R2.11.01.fc12.src.rpm
and atlas3.8.312.fc12.src.rpm
. I crank up the level of optimization that I do when building from source so the first thing is to edit ~/.rpmrc
to include the line optflags: x86_64 O3 march=native m64 g
. With that in place we can simply do:
rpmbuild rebuild SRPMS/R2.11.01.fc12.src.rpm # Change version numbers as needed su c 'rpm Uhv force RPMS/x86_64/R*2.11.01*.rpm RPMS/x86_64/libRmath*2.11.01*.rpm'
We now have a compileroptimized version of R and we can rerun our tests. It doesn’t make much difference, but that is also good to know.
ATLAS BLAS libraries
Now let’s try linking to the ATLAS BLAS libraries instead. I assume you have them installed (yum install atlas
if not) so you can just grab a copy of Ratlas.diff to change the spec file like this:
rpm ihv SRPMS/R2.11.01.fc12.src.rpm # Install to your rpmbuild environment cd SPECS wget http://static.cybaea.net/files/Ratlas.diff patch o Ratlas.spec R.spec Ratlas.diff cd .. rpmbuild bb SPECS/Ratlas.spec su c 'rpm Uhv force RPMS/x86_64/R*2.11.01*.rpm RPMS/x86_64/libRmath*2.11.01*.rpm'
You now have a version of R that uses the ATLAS BLAS libraries, so you can rerun the tests. The results are in the table below in the “Optimized R + Standard ATLAS” row.
As expected, the matrix operations from the Rbenchmark25.R
runs a lot faster: they complete in about 3040% of the time, much of which comes from the multithreading so all four CPU cores are used.
However, for the analysisheavy code is MASSex.R
there is little difference. If anything, we see a tiny increase in running time.
Multithreaded BLAS libraries make no significant difference to realworld analysis problems using R.
Other BLAS libraries
For good measure we also try an optimized version of ATLAS, but it does not make much difference on the x86_64 architecture:
rpmbuild D "enable_native_atlas 1" rebuild SRPMS/atlas3.8.312.fc12.src.rpm su c 'rpm Uhv force RPMS/x86_64/atlas*3.8.312*.rpm'
And (only) for completeness, we also try the standard Netlib BLAS and LAPACK libraries (yum install blas lapack
) by the same method as the ATLAS library above but with a slightly different change to the SPEC file: Rblas.diff
. It performs a little better than vanilla R.
For more information about rebuilding R with different BLAS libraries, see the linear algebra section in the R Installation and Administration manual.
Benchmark results
R version  MASSex.R  R benchmark 2.5  

Real  Total time  Overall mean  Ⅰ. Matrix calc.  Ⅱ. Matrix functions  Ⅲ. Program.  
secs  index  secs  index  secs  index  secs  index  secs  index  secs  index  
Base install  19.00  1.00  78.49  1.00  2.11  1.00  2.32  1.00  3.86  1.00  1.05  1.00 
Optimized R  18.98  1.00  76.11  0.97  2.02  0.96  2.36  1.02  3.46  0.90  1.02  0.97 
Optimized R + Netlib BLAS  18.56  0.98  73.22  0.93  1.81  0.86  2.36  1.02  2.41  0.62  1.04  0.99 
Optimized R + Standard ATLAS  19.43  1.02  16.74  0.21  0.97  0.46  0.90  0.39  1.04  0.27  0.99  0.95 
Optimized R + Optimized ATLAS  19.31  1.02  16.36  0.21  0.95  0.45  0.84  0.36  1.02  0.26  1.00  0.95 
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