English

Compression challenges in large scale PDE solvers

Numerical Analysis 2019-09-19 v3 Distributed, Parallel, and Cluster Computing Information Theory Numerical Analysis math.IT

Abstract

Solvers for partial differential equations (PDE) are one of the cornerstones of computational science. For large problems, they involve huge amounts of data that needs to be stored and transmitted on all levels of the memory hierarchy. Often, bandwidth is the limiting factor due to relatively small arithmetic intensity, and increasingly so 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 during the last decades. This paper surveys data compression challenges and corresponding solution approaches for PDE problems, covering all levels of the memory hierarchy from mass storage up to main memory. Exemplarily, we illustrate concepts at particular methods, and give references to alternatives.

Keywords

Cite

@article{arxiv.1907.00667,
  title  = {Compression challenges in large scale PDE solvers},
  author = {Sebastian Götschel and Martin Weiser},
  journal= {arXiv preprint arXiv:1907.00667},
  year   = {2019}
}

Comments

revised version

R2 v1 2026-06-23T10:08:28.910Z