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

被引：1

作者：

Askar, Tair ^{[1
,2
]}

Yergaliyev, Argyn ^{[3
]}

Shukirgaliyev, Bekdaulet ^{[2
,4
,5
,6
]}

Abdikamalov, Ernazar ^{[2
,3
]}

机构：

[1] Nazarbayev Univ, Sch Engn & Digital Sci, Astana 010000, Kazakhstan

[2] Nazarbayev Univ, Energet Cosmos Lab, Astana 010000, Kazakhstan

[3] Nazarbayev Univ, Dept Phys, Astana 010000, Kazakhstan

[4] Zhubanov Univ, Heriot Watt Int Fac, Aktobe 030000, Kazakhstan

[5] Fesenkov Astrophys Inst, Alma Ata 050020, Kazakhstan

[6] Astana IT Univ, Dept Computat & Data Sci, Astana 010000, Kazakhstan

来源：

COMPUTATION | 2024年 / 12卷 / 03期

关键词：

GPU; CUDA; Numba; CuPy; performance; DOMAIN DECOMPOSITION; SIMULATIONS; IMPLEMENTATION; MODEL; CUDA;

D O I：

10.3390/computation12030061

中图分类号：

O1 [数学];

学科分类号：

0701 ; 070101 ;

摘要：

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.

引用

页数：13

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