There are several implementations of the Basic Linear Algebra Subprograms (BLAS) libraries available. These provide highly optimized routines for matrix and vector operations and are a key to high performance applications.

We recommend to use one of the fastest available libraries:

• GOTO BLAS by Kazushige Goto (http://www.tacc.utexas.edu/tacc-projects/gotoblas2) available in the servers and compute nodes by

  -L/opt/goto/ifort -lgoto2_barcelonap-r1.13

You may want to set THREADS=1.

• Intel Math Kernel Library (MKL): to use e.g. with

  -L/opt/intel/composerxe/mkl/lib/intel64/ -lmkl_intel_lp64 -lmkl_sequential -lmkl_core

  or – with the Intel compiler suite – by simply using

  -mkl

• AMD Core Math Library (ACML): to use e.g. with

  -L/opt/acml5.1.0/ifort64/lib/ -lacml

The GOTO and MKL libraries exist in single and multi threaded versions.

• To use GOTO multi threaded version use -L/opt/goto/ifort -lgoto2_barcelonap-r1.13 -lpthread
• To use the MKL multi threaded version use -lmkl_threads instead of -lmkl_sequential

The reference BLAS is installed on some nodes (-lblas) but significantly slower. We recommend not to use it, even if available.

If shared libraries are used, setting the variable LD_LIBRARY_PATH is required, e.g.

export LD_LIBRARY_PATH=/opt/goto/ifort:$LD_LIBRARY_PATH