Compiling R from Source with OpenMP, Accelerate and MKL in OS X

May 24, 2013

(This article was first published on Category: R | Everything Counts, and kindly contributed to R-bloggers)

Compiling R from Source in OS X

I set out to find out whether I could speed up R by compiling it from source and:

I also wanted to know how an implicit parallel library, like OpenMP,
performs within explicit parallelism, e.g. calls from the parallel

So I compiled 6 different versions of R 3.01 on OS X and tested them
for speed against the OS X .pkg from CRAN. I used gcc 4.8 (not the gcc
4.2 from Apple), which you can download from here. For MKL, I used
both the GNU (gcc, gfortran) and the Intel compilers (icc,
ifortran). The 6 versions and their configure settings are:

  1. R 3.01 compiled with Apple´s Accelerate framework
  2. R 3.01 compiled with OpenMP enabled
  3. R 3.01 compiled with gcc and gfortran using Intel MKL (sequential)
  4. R 3.01 compiled with icc and ifortran using Intel MKL (sequential)
  5. R 3.01 compiled with gcc and gfortran using Intel MKL (threaded)
  6. R 3.01 compiled with icc and ifortran using Intel MKL (threaded)


To measure the speed of the 7 Versions of R I now had (6 compiled, 1
.pkg from CRAN), I used Simon Urbanek´s R Benchmark 2.5. I let it
execute serially for 8 runs, and then in parallel with 4 cores and
2 runs each (so also 8 in total). Additionally, I let each version of
R carry out a large matrix multiplication (10000 rows and 5000
columns). Click here to view the benchmark script. I ran the tests on
an Intel Core i5 clocked at 2.9 GHz with 16 GB RAM.


Elapsed time in seconds for various R builds and benchmarks under OS X:

R 3.01 Bench. 2.5 Bench. 2.5 mclapply Matrix mult.
CRAN OS X .pkg 421.839 154.851 406.383
Accelerate with gcc 107.624 NA 15.730
OpenMP enabled with gcc 108.003 NA 14.530
MKL sequential compiled with gcc 107.513 NA 15.449
MKL sequential compiled with icc 133.528 NA 15.197
MKL threaded compiled with gcc 111.821 NA 14.694
MKL threaded compiled with icc 136.711 NA 15.033

So what do all the NAs mean? None of the R versions I compiled could
execute mclapply() without crashing. If anyone knows how to fix this,
please drop me a message. The benchmarks of the R versions that did
run were much faster though. The Matrix multiplication was on
average 2700% faster, and the more diversified R Benchmark was around
400% faster than stock R on OS X for the optimized
libraries. Nevertheless, if I cannot fix the crashes that occur with
the parallel library, I am going to stick with the stock R version
from CRAN. Any suggestions to improve the compiler options or tests to
add to the profiling script are very welcome.

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