This article describes how we manage to increase performance and to extend features of a large parallel application through the use of simultaneous multithreading (SMT) and by designing a robust parallel transpose algorithm. The semi-Lagrangian code Gysela typically performs large physics simulations using a few thousands of cores, between 1k cores up to 16k on x86-based clusters. However, simulations with finer resolutions and with kinetic electrons increase those needs by a huge factor, providing a good example of applications requiring Exascale machines. To improve Gysela compute times, we take advantage of efficient SMT implementations available on recent INTEL architectures. We also analyze the cost of a transposition communication scheme that involves a large number of cores in our case. Adaptation of the code for balance load whenever using both SMT and good deployment strategy led to a significant reduction that can be up to 38% of the execution times.

This work was strongly supported by the Energy oriented Centre of Excellence (EoCoE), grant agreement number 676629, funded within the Horizon 2020 framework of the European Union. We gratefully acknowledge the POP project, that has also received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 676553. This work was possible due to the generous computational and software engineering supports from FZJ Juelich (Germany) and CCRT Bruyères- le-Châtel (France). The authors would also like to thank Chantal Passeron for assistance and for her precious help.

Peer Reviewed