Compilers and Parallel Programming Models

Presenter: Alfio Lazzaro (HPE)

Archived materials on LUMI:

• Slides: /appl/local/training/4day-20240423/files/LUMI-4day-20240423-1_05_Compilers_and_Parallel_Programming_Models.pdf

• Recording: /appl/local/training/4day-20240423/recordings/1_05_Compilers_and_Parallel_Programming_Models.mp4

These materials can only be distributed to actual users of LUMI (active user account).

Alternative for modifying LD_LIBRARY_PATH

Instead of using LD_LIBRARY_PATH=$CRAY_LD_LIBRARY_PATH:$LD_LIBRARY_PATH, you can also use the module lumi-CrayPath. Loading it essentially does that setting for LD_LIBRARY_PATH, unloading tries to restore the old situation, and reloading while the module is already loaded, will adapt to a possibly modified $CRAY_LD_LIBRARY_PATH. (So basically use it after loading all other modules that you need.)

Q&A

1. Should we compile something on access node?

   • It is usually fine. Cray Programming environment (PrgEnv- modules) does cross-compilation but you need to understand what is your target architecture (craype- modules are responsible for that). For clarity LUMI Software Environment provides partition modules /L (for login nodes architecture) and /C, /G for CPU and AMD GPU nodes accordingly.

   • Very big compiles should be done in a job.

   • The last talk on day 2 will also give some more information about environments we offer that make configuring the target modules easier.

2. Can I write a code in which offloading is performed with HIP and CPU parallelization is with OpenMP in the same source code?

   • Yes, you can. In this case you have to add -fopenmp -xhip in this order.

3. Does LibSci have a sparse solver like pardiso solver in MKL? If no, does LUMI have a sparse solver of any kind in the modules?

   • From AMD point of view check https://github.com/ROCm/hipSPARSE, HPE can answer for the LibSci.

   • No in libsci, only dense

   • If any particular libraries are of interest then we would be able to suggest the best way to build them.