Chana: A GPU-Accelerated Adaptive-Mesh Code for General Relativistic Magnetohydrodynamics with Multigroup M1 Neutrino Transport
Year: 2026
Type: preprint
Abstract: This paper presents Chana, a GPU-accelerated, block-structured adaptive-mesh code for general relativistic magnetohydrodynamics, multigroup M1 neutrino transport, and dynamical spacetime evolution using the Z4c formulation. Each physical module uses independently selected spatial discretizations and ghost-zone widths to balance accuracy and computational cost. Stiff radiation-matter interactions are treated implicitly, with Jacobians calculated analytically from the chain-rules. The code is written in C++20 and it uses MPI and Kokkos 5 for distributed-memory and performance-portable parallelism. The code is validated through a number of GRMHD, radiation transport, and dynamic spacetime tests. For a representative core-collapse supernova configuration with three neutrino species and 12 energy groups per species, the code achieves approximately $1.1 \times 10^6$ cell updates per GPU second on NVIDIA A100 GPUs using four-stage RK4 integration. Weak-scaling efficiency remains above 85\% up to 256 GPUs on Perlmutter.
Source: arXiv (Cornell University)
Author: Wang, Tianshu
Institution: University of California, Berkeley
Language: English
Subfield: Hardware and Architecture
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Awards: G102163711, G107546177, G1313983767, New Theoretical Insights into Cosmic Cataclysms, G1643223109, G1664757510, G193590374, Computation for the Endless Frontier, G2287611682, G262962599, G2710870262, G2787076192, G3075337988, G498139845, G513797252, G6918803902, G7337822738, G8099852790, G993149817
OpenAlex ID: W7220967564