TY - JOUR
T1 - Exploring Numba and CuPy for GPU-Accelerated Monte Carlo Radiation Transport
AU - Askar, Tair
AU - Yergaliyev, Argyn
AU - Shukirgaliyev, Bekdaulet
AU - Abdikamalov, Ernazar
N1 - Publisher Copyright:
© 2024 by the authors.
PY - 2024/3
Y1 - 2024/3
N2 - 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.
AB - 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.
KW - CUDA
KW - CuPy
KW - GPU
KW - Numba
KW - performance
UR - http://www.scopus.com/inward/record.url?scp=85188796542&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85188796542&partnerID=8YFLogxK
U2 - 10.3390/computation12030061
DO - 10.3390/computation12030061
M3 - Article
AN - SCOPUS:85188796542
SN - 2079-3197
VL - 12
JO - Computation
JF - Computation
IS - 3
M1 - 61
ER -
