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)

Indeed, 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):

• 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.