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Open AccessFeature PaperArticle

Compression Challenges in Large Scale Partial Differential Equation Solvers

Zuse Institute Berlin, Takustr. 7, 14195 Berlin, Germany
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Algorithms 2019, 12(9), 197; https://doi.org/10.3390/a12090197
Received: 28 June 2019 / Revised: 12 September 2019 / Accepted: 14 September 2019 / Published: 17 September 2019
(This article belongs to the Special Issue Data Compression Algorithms and their Applications)
Solvers for partial differential equations (PDEs) are one of the cornerstones of computational science. For large problems, they involve huge amounts of data that need to be stored and transmitted on all levels of the memory hierarchy. Often, bandwidth is the limiting factor due to the relatively small arithmetic intensity, and increasingly due to the growing disparity between computing power and bandwidth. Consequently, data compression techniques have been investigated and tailored towards the specific requirements of PDE solvers over the recent decades. This paper surveys data compression challenges and discusses examples of corresponding solution approaches for PDE problems, covering all levels of the memory hierarchy from mass storage up to the main memory. We illustrate concepts for particular methods, with examples, and give references to alternatives. View Full-Text
Keywords: partial differential equation; data compression; floating point compression; lossy compression partial differential equation; data compression; floating point compression; lossy compression
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Götschel, S.; Weiser, M. Compression Challenges in Large Scale Partial Differential Equation Solvers. Algorithms 2019, 12, 197.

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