Exploring Numba and CuPy for GPU-Accelerated Monte Carlo Radiation Transport

Tair Askar, Argyn Yergaliyev, Bekdaulet Shukirgaliyev, Ernazar B. Abdikamalov

2024

Full text Open access: Gold Metadata (CSL-JSON)

DOI 10.3390/computation12030061

Data confidence 70 medium

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Abstract

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.