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Performance evaluation of explicit finite difference algorithms with varying amounts of computational and memory intensity

Performance evaluation of explicit finite difference algorithms with varying amounts of computational and memory intensity
Performance evaluation of explicit finite difference algorithms with varying amounts of computational and memory intensity
Future architectures designed to deliver exascale performance motivate the need for novel algorithmic changes in order to fully exploit their capabilities. In this paper, the performance of several numerical algorithms, characterised by varying degrees of memory and computational intensity, are evaluated in the context of finite difference methods for fluid dynamics problems. It is shown that, by storing some of the evaluated derivatives as single thread- or process-local variables in memory, or recomputing the derivatives on-the-fly, a speed-up of ~2 can be obtained compared to traditional algorithms that store all derivatives in global arrays.
1877-7503
1-15
Jammy, Satya
5267fe44-6c22-473c-b9f0-8e1df884fada
Jacobs, Christian
0ffde78b-6ae2-4b44-a916-666f6be2b92c
Sandham, Neil
0024d8cd-c788-4811-a470-57934fbdcf97
Jammy, Satya
5267fe44-6c22-473c-b9f0-8e1df884fada
Jacobs, Christian
0ffde78b-6ae2-4b44-a916-666f6be2b92c
Sandham, Neil
0024d8cd-c788-4811-a470-57934fbdcf97

Jammy, Satya, Jacobs, Christian and Sandham, Neil (2016) Performance evaluation of explicit finite difference algorithms with varying amounts of computational and memory intensity. Journal of Computational Science, 36, 1-15. (doi:10.1016/j.jocs.2016.10.015).

Record type: Article

Abstract

Future architectures designed to deliver exascale performance motivate the need for novel algorithmic changes in order to fully exploit their capabilities. In this paper, the performance of several numerical algorithms, characterised by varying degrees of memory and computational intensity, are evaluated in the context of finite difference methods for fluid dynamics problems. It is shown that, by storing some of the evaluated derivatives as single thread- or process-local variables in memory, or recomputing the derivatives on-the-fly, a speed-up of ~2 can be obtained compared to traditional algorithms that store all derivatives in global arrays.

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More information

Accepted/In Press date: 27 October 2016
e-pub ahead of print date: 10 November 2016
Organisations: Aerodynamics & Flight Mechanics Group

Identifiers

Local EPrints ID: 402148
URI: http://eprints.soton.ac.uk/id/eprint/402148
ISSN: 1877-7503
PURE UUID: 04c280d5-61c5-49b6-81be-d7caaff031b5
ORCID for Satya Jammy: ORCID iD orcid.org/0000-0002-8099-8573
ORCID for Christian Jacobs: ORCID iD orcid.org/0000-0002-0034-4650
ORCID for Neil Sandham: ORCID iD orcid.org/0000-0002-5107-0944

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Date deposited: 02 Nov 2016 13:58
Last modified: 16 Mar 2024 03:03

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Contributors

Author: Satya Jammy ORCID iD
Author: Christian Jacobs ORCID iD
Author: Neil Sandham ORCID iD

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