Unified Programming Models for Heterogeneous High-Performance Computers

Ma, Zixuan; Jin, Yuyang; Tang, Shizhi; Wang, Haojie; Xue, Weicheng; Zhai, Jidong; Zheng, Weimin

doi:10.1007/s11390-023-2888-4

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2024

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“…These investigations compare the performance and highlight the strength and weaknesses of popular programming platforms such as CUDA C [26][27][28], CUDA Fortran [29][30][31], OpenCL [32][33][34], OpenACC [35,36], OpenMP [37,38] and Python-based compilers and libraries like Numba, CuPy, and Python CUDA [39][40][41][42][43][44]. Despite these efforts, a quest for a paradigm that offers simplicity of implementation and portability in combination with high performance remains one of the main goals of scientific computing (e.g., [45][46][47][48]).…”

Section: Introductionmentioning

confidence: 99%

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

Askar,

Yergaliyev,

Shukirgaliyev

et al. 2024

Computation

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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.

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