R: Runtime-configuration BLAS/LAPACK

The R Project for Statistical Computing is used on our clusters by a wide variety of scientific disciplines. Though the breadth of applications is wide, many of them require the functionality of BLAS/LAPACK libraries. R provides its own baseline implementations that will build on any system; naturally, one cannot expect these BLAS/LAPACK libraries to be highly performant relative to implementations like:

• Intel Math Kernel Library (MKL)
• Automatically-Tuned Linear Algebra Software (ATLAS)

The build procedure for R allows the package to be configured for building against external BLAS/LAPACK libraries. Once the base R build has completed and the resulting software has been installed, additional R libraries can be configured and installed atop it. It has been noted in the past that:

1. Producing N such builds of R that vary only in the choice of underlying BLAS/LAPACK:
   • can require on the order of N times the disk space of a single build
   • puts a greater burden on the sysadmin to maintain all N similarly-outfitted copies
2. R only makes use of standardized BLAS/LAPACK APIs, so any standard BLAS/LAPACK library should be able to be chosen at runtime (not just build time)/

Others have published articles in the past detailing the substitution of the ATLAS library by doing the following to a basic R build (which was built with its bundled BLAS/LAPACK):

• https://www.r-bloggers.com/r-r-with-atlas-r-with-openblas-and-revolution-r-open-which-is-fastest/
• https://stackoverflow.com/questions/29984141/does-installing-blas-atlas-mkl-openblas-will-speed-up-r-package-that-is-written

The basic idea is:

• copy libatlas.so to R_PREFIX/lib64/R/lib
• remove libRblas.so and libRlapack.so from R_PREFIX/lib64/R/lib
• symlink libRblas.so and libRlapack.so to libatlas.so in R_PREFIX/lib64/R/lib

This copy of R is configured to use R_PREFIX/lib64/R/lib to resolve shared libraries, so when executing the R command, for example, the symlinks will lead the runtime linker to the ATLAS library when resolving BLAS/LAPACK functions.

This scheme requires two things:

1. the user must have ownership of the R installation or sufficient privileges to alter the files
2. the BLAS/LAPACK substitution will happen on time only (probably shortly after the library is built)

While the first condition is obvious, the second may not seem important, especially for a build of R being maintained by an arbitrary user in an arbitrary location on the filesystem. However, computational reproducibility would demand that any alteration to the underlying BLAS/LAPACK be present – or at least able to be restored – at any time. This is one reason why libatlas.so was copied into the build and symlinks were used: having other BLAS/LAPACK libraries present, the libRblas.so and libRlapack.so symlinks can be altered as necessary. The caveat, however, is that:

• only a single choice of underlying BLAS/LAPACK can be active
• the underlying BLAS/LAPACK can be changed only when that build of R is not being executed/used

A simple way to organize multiple underlying BLAS/LAPACK libraries in a single R installation is to create subdirectories for each variant: