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2022•Unpublished venueOpen access

Python Development Schemes for Monte Carlo Neutronics on High Performance Computing

Joanna Piper Morgan, Kyle Evan Niemeyer

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Abstract

We investigate three methods of hardware accleeration on both GPUs and CPUs for a Monte Carlo neutron transport simulation code writen in Python. The accelerating schemes we examine are Pykokks, Numba, and hardware code generating libraries like PyCUDA. This work was supported by the Center for Exascale Monte-Carlo Neutron Transport (CEMeNT) a PSAAP-III project funded by the Department of Energy, grant number: DE-NA003967.

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We investigate three methods of hardware accleeration on both GPUs and CPUs for a Monte Carlo neutron transport simulation code writen in Python. The accelerating schemes we examine are Pykokks, Numba, and hardware code generating libraries like PyCUDA. This work was supported by the Center for Exascale Monte-Carlo Neutron Transport (CEMeNT) a PSAAP-III project funded by the Department of Energy, grant number: DE-NA003967.

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Available abstract

We investigate three methods of hardware accleeration on both GPUs and CPUs for a Monte Carlo neutron transport simulation code writen in Python. The accelerating schemes we examine are Pykokks, Numba, and hardware code generating libraries like PyCUDA. This work was supported by the Center for Exascale Monte-Carlo Neutron Transport (CEMeNT) a PSAAP-III project funded by the Department of Energy, grant number: DE-NA003967.

Key concepts: Neutron transport, Python (programming language), Monte Carlo method, Computer science, Computational science, Parallel computing, Radiation transport, Neutron

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