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AsterX: a new open-source GPU-accelerated GRMHD code for dynamical spacetimes

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arxiv 2406.11669 v2 pith:I2TUYF5N submitted 2024-06-17 astro-ph.HE gr-qc

Jay V. Kalinani , Liwei Ji , Lorenzo Ennoggi , Federico G. Lopez Armengol , Lucas Timotheo Sanches , Bing-Jyun Tsao , Steven R Brandt , Manuela Campanelli
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Riccardo Ciolfi Bruno Giacomazzo Roland Haas Erik Schnetter Yosef Zlochower
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classification astro-ph.HEgr-qc
keywords asterxcodegrmhddynamicgpu-acceleratedopen-sourceperformancescaling
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We present AsterX, a novel open-source, modular, GPU-accelerated, fully general relativistic magnetohydrodynamic (GRMHD) code designed for dynamic spacetimes in 3D Cartesian coordinates, and tailored for exascale computing. We utilize block-structured adaptive mesh refinement (AMR) through CarpetX, the new driver for the Einstein Toolkit, which is built on AMReX, a software framework for massively parallel applications. AsterX employs the Valencia formulation for GRMHD, coupled with the Z4c formalism for spacetime evolution, while incorporating high resolution shock capturing schemes to accurately handle the hydrodynamics. AsterX has undergone rigorous testing in both static and dynamic spacetime, demonstrating remarkable accuracy and agreement with other codes in literature. Using subcycling in time, we find an overall performance gain of factor 2.5 to 4.5. Benchmarking the code through scaling tests on OLCF's Frontier supercomputer, we demonstrate a weak scaling efficiency of about 67%-77% on 4096 nodes compared to an 8-node performance.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. From Multimessenger Inference to Simulations: A Ranked Ensemble of Finite-Temperature Equations of State

    astro-ph.HE 2026-08 conditional novelty 7.0 of 10

    The paper constructs a 12-member ensemble of finite-temperature neutron star equations of state that spans the posterior from multimessenger and nuclear-physics constraints and releases simulation-ready tables.

  2. GRACE: An Open-Source Framework for GPU-Accelerated Numerical Relativity

    gr-qc 2026-07 accept novelty 6.0 of 10

    GRACE is a validated, open-source, Kokkos+p4est GPU-portable framework that evolves ideal GRMHD with constrained transport self-consistently coupled to Z4c Einstein equations on fixed or adaptive meshes.

  3. A "Neural" Riemann solver for Relativistic Hydrodynamics

    gr-qc 2025-05 conditional novelty 6.0 of 10

    Small neural networks trained on exact solutions can replace the iterative root-finding in a relativistic Riemann solver, giving exact-like accuracy about 14 times faster in 1D tests.

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