A Comparison of Computing Spectral Transforms of Logic Functions using Python Frameworks on GPU

Radmanovic, Milos

doi:10.1109/icest55168.2022.9828786

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2024

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“…CuPy provides a simple NumPy-like interface with GPU acceleration [16,17]. Its compatibility with existing Python libraries and support for a wide range of mathematical operations have made it a popular choice for scientific computing tasks (e.g., [82,83]).…”

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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Section: Introductionmentioning

confidence: 99%