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Evaluating GPU Programming Models for the LUMI Supercomputer

Markomanolis, G. S.; Alpay, A.; Young, J.; Klemm, M.; Malaya, N.; Esposito, A.; Heikonen, J.; Bastrakov, S.; Debus, A.; Kluge, T.; Steiniger, K.; Stephan, J.; Widera, R.; Bussmann, M.

Abstract

It is common in the HPC community that the achieved performance with just CPUs is limited for many computational cases. The EuroHPC pre-exascale and the coming exascale systems are mainly focused on accelerators, and some of the largest upcoming supercomputers such as LUMI and Frontier will be powered by AMD Instinct™ accelerators. However, these new systems create many challenges for developers who are not familiar with the new ecosystem or with the required programming models that can be used to program for heterogeneous architectures. In this paper, we present some of the more well-known programming models to program for current and future GPU systems. We then measure the performance of each approach using a benchmark and a mini-app, test with various compilers, and tune the codes where necessary. Finally, we compare the performance, where possible, between the NVIDIA Volta (V100), Ampere (A100) GPUs, and the AMD MI100 GPU.

Keywords: GPU; Programming Models; HIP; CUDA; OpenMP; hipSYCL; Kokkos; Alpaka

  • Open Access Logo Contribution to proceedings
    Supercomputing Frontiers Asia 2022, 01.-03.03.2022, Singapore, Republic of Singapore
    Supercomputing Frontiers, Cham, Schweiz: Springer Nature, 978-3-031-10418-3, 79-101
    DOI: 10.1007/978-3-031-10419-0_6
    Cited 7 times in Scopus

Permalink: https://www.hzdr.de/publications/Publ-34406


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