Basic Linear Algebra Subroutine (BLAS) is a de facto application programming interface standard for publishing libraries to perform basic linear algebra operations such as vector and matrix multiplication (from wikipedia).

On triton a number of different BLAS implementations are available. The recommended ones are MKL and OpenBLAS. Other available BLAS libraries are ATLAS, GotoBLAS2 ,ACML, and the Netlib reference BLAS. For benchmark results see

DGEMM benchmark on triton

Using MKL

In order to use the MKL library you need to load the module. MKL is provided both in the “mkl” modules and in the “intel” modules; the “intel” module additonally give you the Intel compilers and debuggers. Linking with MKL is a bit tricky and the exact link options varies from version to version. Intel provides a webpage to build the correct linking options at .

Using OpenBLAS

OpenBLAS is installed on all the triton nodes on the default library directory (/usr/lib64). 3 different variants of the library are provided:

  • Serial version: Link using “-lopenblas”

  • OpenMP version: Link using “-lopenblaso”

  • pthreads version: Link using “-lopenblasp”

Other BLAS libraries

In general MKL and OpenBLAS are recommended since they both provide good performance on all the node types we have. Other BLAS libraries have various issues such as crashing when running on the incorrect node (e.g. running an Haswell optimized library on an Westmer node or vice versa), or poor performance. In particular, the Netlib reference BLAS has VERY poor performance and should be avoided at all cost. Use it only for testing or if you need to debug numeric output. As can be seen on the DGEMM benchmark results performance for large matrices is an order of magnitude worse than the optimized versions. For a real example see for instance where 50% performance loss was seen for a complete application.


Linear Algebra Package (LAPACK) is a library of numerical linear algebra algorithms, built on top of BLAS. The recommended LAPACK implementation on triton is MKL. Alternatively, our OpenBLAS modules and libraries also contain the LAPACK library compiled agains the OpenBLAS BLAS library.


MKL contains an implementation of ScaLAPACK as well, please try to use that one first. Again, see the BLAS section for how to link with MKL.

Benchmark is done with full Scalapack LIN/EIG testsuite with 24 processors. Scalapack is compiled with -O3 using architecture optimized gotoblas2. Given numbers are WallClockTimes in seconds.


Xeon processors

Opteron processors

Xeon-gcc optimized scalapack



Opteron-gcc optimized scalapack






Scalapck libs are available under /share/apps/scalapack/2.0.1/


The FFTW library is available on triton, in several different variants. The recommended one is the one provided by MKL; see the BLAS section above for how to link to it.



Link line

FFTW 3.2.1

-lfftw3 / -lfftw3_threads / -lfftw3f / -lfftw3f_threads / -lfftw3l / -lfftw3l_threads

FFTW 3.3.2


-lfftw3 / -lfftw3_mpi


intel/VERSION or mkl/VERSION