Software environment on Betzy

As on Fram and Saga, scientific software on Betzy will be installed using the EasyBuild system, and the Lmod modules tool will be used for changing environment setup via modulefiles.

The two common toolchains foss and intel will be installed on Betzy.

foss toolchain

  • GCC compilers (gcc, g++, gfortran)

  • Open MPI library

  • OpenBLAS (including LAPACK) + ScaLAPACK

  • FFTW library

intel toolchain

  • Intel compilers (icc, icpc, ifort)

  • Intel MPI library

  • Intel MKL library (including BLAS, LAPACK, ScaLAPACK, FFT)

Regarding compiler optimization this needs to be investigated case by case. Aggressive optimization should be added only to files where it makes a difference, as it increases the probability of the compiler generating wrong code or exposing floating-point issues in the application. A few starting suggestions are:

Compiler flags

A set of suggested compiler flags are given below, these have been tested and used, but users are advised to read the documentation and try other combinations as not all code are alike.


  • -O3

  • -O3 -march=znver2 -mtune=znver2 (recommended)

  • -O3 -march=znver2 -mtune=znver2 -mfma -mavx2 -m3dnow -fomit-frame-pointer


  • -O3

  • -O3 -march=core-avx2

  • -O3 -xavx

  • -O3 -xavx2 (recommended, for all execpt for main() )

  • -O3 -xcore-avx2

The above applies to GCC 9.3 and Intel 2019b, and the choices are listed in increased performance as obtained with a DGEMM matrix multiplication test (which show significant performance improvement), it is also verified using one of the major benchmarks used. Please notice that ifort performs substantially better (2-4x) than gfortran with the dgemm.f test. Also, building the main routine in the program file with -xcore-avx2, -xavx or -xavx2 is not recommended. It’s known that building the main() (C and Fortran) with these flags trigger the Intel processor run time check, causing the application to abort.

MPI libraries

Both OpenMPI and Intel MPI are installed. Built both for GNU and Intel. Experience have shown that performance varies. Both OpenMPI and Intel MPI are supported. OpenMPI is built with support for GNU compiler and Intel.


  • mpicc

  • mpicxx

  • mpif90

For running with OpenMPI the processor binding is beneficial, adding -bind-to core is generally a good idea.

Intel MPI

  • mpiicc

  • mpiicpc

  • mpiifort

For running Intel MPI, these settings are good starting points:

  • I_MPI_PIN=1

  • I_MPI_PIN_PROCESSOR_EXCLUDE_LIST=128-255 (run only on physical cores)

For a correct Slurm job script file the only command needed to launch MPI programs are for :

  • OpenMPI : mpirun -bind-to core ./a.out

  • Intel MPI: mpirun ./a.out

Running Hybrid models

For hybrid models it’s important to set up Slurm to provide access to all available cores. An example could look like this:

#SBATCH --ntasks=2048
#SBATCH --nodes=64
#SBATCH --ntasks-per-node=32
#SBATCH --cpus-per-task=4
#SBATCH --exclusive

This will request 32 MPI ranks per node, and leave cores for 4 threads per rank, e.g. each of the 32 ranks can schedule 4 threads yielding a total of 128 cores which is the maximum number of cores on each compute node. The exclusive is important, if not set Slurm will only allow the 32 cores allocated to be used (this will place all 4 threads onto one core). In order to have free access to all cores the exclusive need to be set.

MKL library

The MKL perform run time check to select correct code to invoke. This test fail to find any Intel processor and hence select a code compatible with all x86-64 processors. Setting the environment flag MKL_DEBUG_CPU_TYPE=5 will force MKL to select code that uses AVX2 instructions, hence increase performance significantly.

It is possible to use Intel MKL library with the GNU compilers, this require some work resolve the symbol names at link time and library path at run time, but can provide a nice performance boost to both linear algebra and Fourier transforms.