Work

Compression Challenges in Large Scale Partial Differential Equation Solvers

Year: 2019

Type: article

Abstract: 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.

Source: Algorithms

DOI: 10.3390/a12090197

Authors: Sebastian Götschel, Martin Weiser

Institution: Zuse Institute Berlin

Language: English

Topic: Parallel Computing and Optimization Techniques

Subfield: Hardware and Architecture

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $1,013

Funder: Bundesministerium für Bildung und Forschung