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Abstract We presentAsterX, 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) throughCarpetX, the new driver for theEinstein Toolkit, which is built onAMReX, a software framework for massively parallel applications.AsterXemploys 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.AsterXhas 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 2.5–4.5. 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. 

We present GRaM-X (General Relativistic accelerated Magnetohydrodynamics on AMReX), a new GPU-accelerated dynamical-spacetime general relativistic magnetohydrodynamics (GRMHD) code which extends the GRMHD capability of Einstein Toolkit to GPU-based exascale systems. GRaM-X supports 3D adaptive mesh refinement (AMR) on GPUs via a new AMR driver for the Einstein Toolkit called CarpetX which in turn leverages AMReX, an AMR library developed for use by the United States DOE's Exascale Computing Project. We use the Z4c formalism to evolve the Einstein equations and the Valencia formulation to evolve the equations of GRMHD. GRaM-X supports both analytic as well as tabulated equations of state. We implement TVD and WENO reconstruction methods as well as the HLLE Riemann solver. We test the accuracy of the code using a range of tests on static spacetime, e.g. 1D magnetohydrodynamics shocktubes, the 2D magnetic rotor and a cylindrical explosion, as well as on dynamical spacetimes, i.e. the oscillations of a 3D Tolman-Oppenheimer-Volkhof star. We find excellent agreement with analytic results and results of other codes reported in literature. We also perform scaling tests and find that GRaM-X shows a weak scaling efficiency of ∼40%–50% on 2304 nodes (13824 NVIDIA V100 GPUs) with respect to single-node performance on OLCF's supercomputer Summit. 

Abstract We present a 3D general-relativistic magnetohydrodynamic simulation of a short-lived neutron star remnant formed in the aftermath of a binary neutron star merger. The simulation uses an M1 neutrino transport scheme to track neutrino–matter interactions and is well suited to studying the resulting nucleosynthesis and kilonova emission. A magnetized wind is driven from the remnant and ejects neutron-rich material at a quasi-steady-state rate of 0.8 × 10⁻¹M_⊙s⁻¹. We find that the ejecta in our simulations underproducer-process abundances beyond the secondr-process peak. For sufficiently long-lived remnants, these outflowsalonecan produce blue kilonovae, including the blue kilonova component observed for AT2017gfo. 

Abstract We presentGRaM-X(GeneralRelativisticacceleratedMagnetohydrodynamics on AMReX), a new GPU-accelerated dynamical-spacetime general relativistic magnetohydrodynamics (GRMHD) code which extends the GRMHD capability of Einstein Toolkit to GPU-based exascale systems.GRaM-Xsupports 3D adaptive mesh refinement (AMR) on GPUs via a new AMR driver for the Einstein Toolkit calledCarpetXwhich in turn leveragesAMReX, an AMR library developed for use by the United States DOE’s Exascale Computing Project. We use the Z4c formalism to evolve the Einstein equations and the Valencia formulation to evolve the equations of GRMHD.GRaM-Xsupports both analytic as well as tabulated equations of state. We implement TVD and WENO reconstruction methods as well as the HLLE Riemann solver. We test the accuracy of the code using a range of tests on static spacetime, e.g. 1D magnetohydrodynamics shocktubes, the 2D magnetic rotor and a cylindrical explosion, as well as on dynamical spacetimes, i.e. the oscillations of a 3D Tolman-Oppenheimer-Volkhof star. We find excellent agreement with analytic results and results of other codes reported in literature. We also perform scaling tests and find thatGRaM-Xshows a weak scaling efficiency of ∼40%–50% on 2304 nodes (13824 NVIDIA V100 GPUs) with respect to single-node performance on OLCF’s supercomputer Summit.