A case study of scientific computing with limited precision numbers: Applying rescaling and baseline shifting techniques to a spectral Vlasov–Maxwell code

We have developed SpectaVla, a code that solves the multi-dimensional Vlasov–Maxwell equations using the spectral method described in G. L. Delzanno [J. Comp. Phys. 301, 338 (2015)]. This code is designed to take advantage of a chip-scale tensor core optimized for 3D convolution with 8-bit fixed-point numbers. The code is benchmarked against theory on a number of fundamental plasma physics problems. We employ rescaling and baseline shifting techniques to ensure proper precision of the results when using 8-bit fixed-point numbers.

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Publication Details

Journal
Physics of Plasmas
Published
2025-04-01
DOI
https://doi.org/10.1063/5.0254812
Citations
1
Primary Topic
Neural Networks and Reservoir Computing
Type
article
Field-Weighted Citation Impact
2.48

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article

A case study of scientific computing with limited precision numbers: Applying rescaling and baseline shifting techniques to a spectral Vlasov–Maxwell code

Sasanka Munasinghe, Shihui Cao, Michael Huang, Hui Wu et al.
1 citations
Physics of Plasmas
Neural Networks and Reservoir Computing
2.48
article

A case study of scientific computing with limited precision numbers: Applying rescaling and baseline shifting techniques to a spectral Vlasov–Maxwell code

Sasanka Munasinghe, Shihui Cao, Michael Huang, Hui Wu, Chuang Ren, Q. Lin
article en
1 citations

Abstract

We have developed SpectaVla, a code that solves the multi-dimensional Vlasov–Maxwell equations using the spectral method described in G. L. Delzanno [J. Comp. Phys. 301, 338 (2015)]. This code is designed to take advantage of a chip-scale tensor core optimized for 3D convolution with 8-bit fixed-point numbers. The code is benchmarked against theory on a number of fundamental plasma physics problems. We employ rescaling and baseline shifting techniques to ensure proper precision of the results when using 8-bit fixed-point numbers.

Physics of PlasmasVol. 32(4)
University of Rochester (US)
Defense Sciences Office, DARPA
Openalex Percentile: Top 11%
Neural Networks and Reservoir Computing
2.48
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