Does Installing Blas/Atlas/Mkl/Openblas Will Speed Up R Package That Is Written in C/C++

Does installing BLAS/ATLAS/MKL/OPENBLAS will speed up R package that is written in C/C++?

It is frequently stated, including in a comment here, that "you have to recompile R" to use different BLAS or LAPACK library. That is wrong.

You do not have to recompile R provided it is build against the shared library versions of BLAS and LAPACK.

I have a package and vignette on CRAN which uses this fact to provide a benchmarking framework in which different BLAS and LAPACK version are timed against each just by installing different ones (one commmand in Debian/Ubuntu) and running benchmarks -- this is so straightforward that it can be automated in a package such as this.

The results in that package will provide an idea of the possible speed differences. Exactly how they pan out depends on your computer, your data (size), your problem etc. But if, say, your problem uses LAPACK functions which can run benefit from running multithreaded then installing OpenBLAS may help. That is true for any R package using LAPACK as they will use the same LAPACK installation accessed through are, and these can be changed.

Large performance differences between OS for matrix computation

tldr: CentOS uses single-threaded OpenBLAS, Linux Mint uses Reference BLAS by default but can use other BLAS versions.

The R packages for CentOS available from EPEL depend on openblas-Rblas. This seems to be an OpenBLAS build providing BLAS for R. So while it looks like R's BLAS is used, it actually is OpenBLAS. The LAPACK version is always the one provided by R.

On Debian and derived distributions like Mint, r-base-core depends on

• libblas3 | libblas.so.3
• liblapack3 | liblapack.so.3

By default these are provided by the reference implementations libblas3 and liblapack3. These are not particularly fast, but you can replace them easily by installing packages like libopenblas-base. You have control over the BLAS and LAPACK used on your system via update-alternatives.

For controlling the number of threads with OpenBLAS I normally use RhpcBLASctl:

N <- 20000
M <- 2000
X <- matrix(rnorm(N * M), N)
RhpcBLASctl::blas_set_num_threads(2)
system.time(crossprod(X))
#>        User      System verstrichen 
#>       2.492       0.331       1.339
RhpcBLASctl::blas_set_num_threads(1)
system.time(crossprod(X))
#>        User      System verstrichen 
#>       2.319       0.052       2.316

For some reason setting the environment variables OPENBLAS_NUM_THREADS, GOTO_NUM_THREADS or OMP_NUM_THREADS from R does not have the desired effect. On CentOS even RhpcBLASctl does not help, since the used OpenBLAS is single-threaded.

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