2024/09/07 by Joanna Piper Morgan, Morgan, Joanna Piper, Ilham Variansyah +7 · 1 citation
Engineering · Physics and Astronomy · #Computational Physics (physics.comp-ph) #FOS: Physical sciences #Magnetic confinement fusion research #Nuclear Physics and Applications #Nuclear reactor physics and engineering
paper · pdf · doi:10.48550/arxiv.2409.04668
openalex publication_date 2024/09/07 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
Finding a software engineering approach that allows for portability, rapid development, and open collaboration for high-performance computing on GPUs and CPUs is a challenge. We implement a portability scheme using the Numba compiler for Python in Monte Carlo / Dynamic Code (MC/DC), a new neutron transport application for rapidly developing Monte Carlo. Using this scheme, we have built MC/DC as an application that can run as a pure Python, compiled CPU, or compiled GPU solver. In GPU mode, we use Numba paired with an asynchronous GPU scheduler called Harmonize to increase GPU performance. We present performance results (including weak scaling up to 256 nodes) for a time-dependent problem on both CPUs and GPUs and compare favorably to a production C++ code.