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Parthenon -- a performance portable block-structured adaptive mesh refinement framework

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arxiv 2202.12309 v3 pith:MRHGH25I submitted 2022-02-24 cs.DC astro-ph.IM

Parthenon -- a performance portable block-structured adaptive mesh refinement framework

Philipp Grete , Joshua C. Dolence , Jonah M. Miller , Joshua Brown , Ben Ryan , Andrew Gaspar , Forrest Glines , Sriram Swaminarayan
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Jonas Lippuner Clell J. Solomon Galen Shipman Christoph Junghans Daniel Holladay James M. Stone Luke F. Roberts
This is my paper
classification cs.DC astro-ph.IM
keywords parthenoncpusexascaleframeworkmeshperformanceportableprogramming
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On the path to exascale the landscape of computer device architectures and corresponding programming models has become much more diverse. While various low-level performance portable programming models are available, support at the application level lacks behind. To address this issue, we present the performance portable block-structured adaptive mesh refinement (AMR) framework Parthenon, derived from the well-tested and widely used Athena++ astrophysical magnetohydrodynamics code, but generalized to serve as the foundation for a variety of downstream multi-physics codes. Parthenon adopts the Kokkos programming model, and provides various levels of abstractions from multi-dimensional variables, to packages defining and separating components, to launching of parallel compute kernels. Parthenon allocates all data in device memory to reduce data movement, supports the logical packing of variables and mesh blocks to reduce kernel launch overhead, and employs one-sided, asynchronous MPI calls to reduce communication overhead in multi-node simulations. Using a hydrodynamics miniapp, we demonstrate weak and strong scaling on various architectures including AMD and NVIDIA GPUs, Intel and AMD x86 CPUs, IBM Power9 CPUs, as well as Fujitsu A64FX CPUs. At the largest scale on Frontier (the first TOP500 exascale machine), the miniapp reaches a total of $1.7\times10^{13}$ zone-cycles/s on 9,216 nodes (73,728 logical GPUs) at ~92% weak scaling parallel efficiency (starting from a single node). In combination with being an open, collaborative project, this makes Parthenon an ideal framework to target exascale simulations in which the downstream developers can focus on their specific application rather than on the complexity of handling massively-parallel, device-accelerated AMR.

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