[R] Using OpenBLAS with R

[Michael Hannon]

Greetings.  I'd like to get some advice about using OpenBLAS with R, rather
than using the BLAS that comes built in to R.

I've tried this on my Fedora 20 system (see the appended for details).  I ran
a simple test -- multiplying two large matrices -- and the results were very
impressive, i.e., in favor of OpenBLAS, which is consistent with discussions
I've seen on the web.

My concern is that maybe this is too good to be true.  I.e., the standard R
configuration is vetted by thousands of people every day.  Can I have the same
degree of confidence with OpenBLAS that I have in the built-in version?

And/or are there other caveats to using OpenBLAS of which I should be aware?

Thanks.

-- Mike

#### Here's the version of R, compiled locally with configuration options:
#### ./configure --enable-R-shlib --enable-BLAS-shlib

$ R

R version 3.1.2 (2014-10-31) -- "Pumpkin Helmet"
Copyright (C) 2014 The R Foundation for Statistical Computing
Platform: x86_64-unknown-linux-gnu (64-bit)
.
.
.

#### Here's the R source code for this little test:

library(microbenchmark)

mSize <- 10000
set.seed(42)

aMat <- matrix(rnorm(mSize * mSize), nrow=mSize)
bMat <- matrix(rnorm(mSize * mSize), nrow=mSize)

cMat <- aMat %*% bMat  ## do the calculation once to see that it works

traceCMat <- sum(diag(cMat))  ## a mild sanity check on the calculation
traceCMat

microbenchmark(aMat %*% bMat, times=5L)  ## repeat a few more times

-----

#### Here is the output from code, running under various conditions:

> traceCMat ###### Using the built-in BLAS from R
[1] -11367.55
> microbenchmark(aMat %*% bMat, times=5L)
Unit: seconds
          expr      min       lq     mean   median       uq     max neval
 aMat %*% bMat 675.0064 675.5325 675.4897 675.5857 675.6618 675.662     5

----------

> traceCMat  ###### Using libopenblas.so from Fedora
[1] -11367.55
> microbenchmark(aMat %*% bMat, times=5L)
Unit: seconds
          expr      min       lq     mean   median       uq      max neval
 aMat %*% bMat 70.67843 70.70545 70.76365 70.73026 70.83935 70.86475     5
>

----------

> traceCMat <- sum(diag(cMat))  ###### libopenblas.so from Fedora with
> traceCMat                     ###### export OMP_NUM_THREADS=6
[1] -11367.55
> microbenchmark(aMat %*% bMat, times=5L)
Unit: seconds
          expr      min       lq    mean   median       uq      max neval
 aMat %*% bMat 69.99146 70.02426 70.3466 70.08327 70.39537 71.23866     5
>

###### Fedora libopenblas.so appears to be single-threaded

----------

> traceCMat <- sum(diag(cMat))  ###### libopenblas.so compiled locally
> traceCMat                     ###### from source w/OMP_NUM_THREADS=6
[1] -11367.55
> microbenchmark(aMat %*% bMat, times=5L)
Unit: seconds
          expr      min       lq     mean   median       uq      max neval
 aMat %*% bMat 26.77385 27.10434 27.17862 27.12485 27.16301 27.72705     5
>

###### Locally-compiled openblas appears to be multi-threaded
###### The microbenchmark appeared to use all 8 processors, even
###### though I asked for only 6.