Exploring Numba and CuPy for GPU-Accelerated Monte Carlo Radiation Transport
Askar T. Yergaliyev A. Shukirgaliyev B. Abdikamalov E.
March 2024Multidisciplinary Digital Publishing Institute (MDPI)
Computation
2024#12Issue 3
This paper examines the performance of two popular GPU programming platforms, Numba and CuPy, for Monte Carlo radiation transport calculations. We conducted tests involving random number generation and one-dimensional Monte Carlo radiation transport in plane-parallel geometry on three GPU cards: NVIDIA Tesla A100, Tesla V100, and GeForce RTX3080. We compared Numba and CuPy to each other and our CUDA C implementation. The results show that CUDA C, as expected, has the fastest performance and highest energy efficiency, while Numba offers comparable performance when data movement is minimal. While CuPy offers ease of implementation, it performs slower for compute-heavy tasks.
CUDA , CuPy , GPU , Numba , performance
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School of Engineering and Digital Sciences, Nazarbayev University, Astana, 010000, Kazakhstan
Energetic Cosmos Laboratory, Nazarbayev University, Astana, 010000, Kazakhstan
Department of Physics, Nazarbayev University, Astana, 010000, Kazakhstan
Heriot-Watt International Faculty, Zhubanov University, Aktobe, 030000, Kazakhstan
Fesenkov Astrophysical Institute, Almaty, 050020, Kazakhstan
Department of Computation and Data Science, Astana IT University, Astana, 010000, Kazakhstan
School of Engineering and Digital Sciences
Energetic Cosmos Laboratory
Department of Physics
Heriot-Watt International Faculty
Fesenkov Astrophysical Institute
Department of Computation and Data Science
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