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DOE OSTI · 3002715

Asynchronous-many-task systems: Challenges and opportunities - Scaling an AMR astrophysics code on exascale machines using Kokkos and HPX

Daiß, Gregor [Univ. of Stuttgart (Germany)]·Diehl, Patrick [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States); Louisiana State Univ., Baton Rouge, LA (United States)] (ORCID:0000000339228419)·Yan, Jiakun [Univ. of Illinois at Urbana-Champaign, IL (United States)]·Holmen, John K. [Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)] (ORCID:0000000259342641)·Gayatri, Rahulkumar [Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)]·Junghans, Christoph [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000309251458)·Straub, Alexander [Univ. of Stuttgart (Germany)] (ORCID:0000000267499710)·Hammond, Jeff R. [NVIDIA Helsinki Oy (Finland)]·Marcello, Dominic [Louisiana State Univ., Baton Rouge, LA (United States)]·Tsuji, Miwako [RIKEN Center for Computational Science, Kobe (Japan)]·Pflüger, Dirk [Univ. of Stuttgart (Germany)]·Kaiser, Hartmut [Louisiana State Univ., Baton Rouge, LA (United States)]
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Abstract

Dynamic and adaptive mesh refinement is pivotal in high-resolution, multi-physics, multi-model simulations, necessitating precise physics resolution in localized areas across expansive domains. Today’s supercomputers’ extreme heterogeneity presents a significant challenge for dynamically adaptive codes, highlighting the importance of achieving performance portability at scale. Our research focuses on astrophysical simulations, particularly stellar mergers, to elucidate early universe dynamics. Here, we present Octo-Tiger, leveraging Kokkos, HPX, and SIMD for portable performance at scale in complex, massively parallel adaptive multi-physics simulations. Octo-Tiger supports diverse processors, accelerators, and network backends. Experiments demonstrate exceptional scalability across several heterogeneous supercomputers including Perlmutter, Frontier, and Fugaku, encompassing major GPU architectures and x86, ARM, and RISC-V CPUs. Parallel efficiency of 47.59% (110,080 cores and 6880 hybrid A100 GPUs) on a full-system run on Perlmutter (26% HPCG peak performance) and 51.37% (using 32,768 cores and 2048 MI250X) on Frontier are achieved.

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97 MATHEMATICS AND COMPUTINGAdaptive mesh refinementAsynchronous-many-task systemsExascale computingHPXHigh performance computingKokkosStellar merger

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Daiß, Gregor [Univ. of Stuttgart (Germany)], Diehl, Patrick [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States); Louisiana State Univ., Baton Rouge, LA (United States)] (ORCID:0000000339228419), Yan, Jiakun [Univ. of Illinois at Urbana-Champaign, IL (United States)], Holmen, John K. [Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)] (ORCID:0000000259342641), Gayatri, Rahulkumar [Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)], Junghans, Christoph [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000309251458), Straub, Alexander [Univ. of Stuttgart (Germany)] (ORCID:0000000267499710), Hammond, Jeff R. [NVIDIA Helsinki Oy (Finland)], Marcello, Dominic [Louisiana State Univ., Baton Rouge, LA (United States)], Tsuji, Miwako [RIKEN Center for Computational Science, Kobe (Japan)], Pflüger, Dirk [Univ. of Stuttgart (Germany)], Kaiser, Hartmut [Louisiana State Univ., Baton Rouge, LA (United States)]. 2025-10-16. Asynchronous-many-task systems: Challenges and opportunities - Scaling an AMR astrophysics code on exascale machines using Kokkos and HPX. https://doi.org/10.1177/10943420251386503

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