Titarenko, S orcid.org/0000-0002-4453-0180 and Hildyard, M (2017) Hybrid Multicore/vectorisation technique applied to the elastic wave equation on a staggered grid. Computer Physics Communications, 216. pp. 53-62. ISSN 0010-4655
Abstract
In modern physics it has become common to find the solution of a problem by solving numerically a set of PDEs. Whether solving them on a finite difference grid or by a finite element approach, the main calculations are often applied to a stencil structure. In the last decade it has become usual to work with so called big data problems where calculations are very heavy and accelerators and modern architectures are widely used. Although CPU and GPU clusters are often used to solve such problems, parallelisation of any calculation ideally starts from a single processor optimisation. Unfortunately, it is impossible to vectorise a stencil structured loop with high level instructions. In this paper we suggest a new approach to rearranging the data structure which makes it possible to apply high level vectorisation instructions to a stencil loop and which results in significant acceleration. The suggested method allows further acceleration if shared memory APIs are used. We show the effectiveness of the method by applying it to an elastic wave propagation problem on a finite difference grid. We have chosen Intel architecture for the test problem and OpenMP (Open Multi-Processing) since they are extensively used in many applications.
Metadata
Item Type: | Article |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | 2017 The Authors. Published by Elsevier B.V. This is an open access article under the CC-BY license, http://creativecommons.org/licenses/by/4.0/. |
Keywords: | OpenMP; Vectorisation; Multicore; Elastic waves; Staggered grid |
Dates: |
|
Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Environment (Leeds) > School of Earth and Environment (Leeds) > Institute for Applied Geosciences (IAG) (Leeds) The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Mathematics (Leeds) > Statistics (Leeds) |
Funding Information: | Funder Grant number NERC NE/L000423/1 |
Depositing User: | Symplectic Publications |
Date Deposited: | 02 Mar 2017 11:03 |
Last Modified: | 22 Oct 2017 06:37 |
Published Version: | https://doi.org/10.1016/j.cpc.2017.02.022 |
Status: | Published |
Publisher: | Elsevier |
Identification Number: | 10.1016/j.cpc.2017.02.022 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:113066 |