GRaM-X: A new GPU-accelerated dynamical spacetime GRMHD code for Exascale computing with the Einstein Toolkit by Swapnil Shankar et al. on Tuesday 22 November

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 (ECP). We use the Z4c

formalism to evolve the equations of GR 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 MHD shocktubes, the 2D magnetic rotor and a

cylindrical explosion, as well as on dynamical spacetimes, i.e. the

oscillations of a 3D TOV 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 $\sim

40-50\%$ on 2304 nodes (13824 NVIDIA V100 GPUs) with respect to single-node

performance on OLCF's supercomputer Summit.

arXiv: http://arxiv.org/abs/http://arxiv.org/abs/2210.17509v2