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Warwick Data Store : a data structure abstraction library
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Kirk, Richard, Nolten, Martin, Kevis, Robert, Law, Timothy R., Maheswaran, Satheesh, Wright, Steven A., Powell, Seimon, Mudalige, Gihan R. and Jarvis, Stephen A. (2021) Warwick Data Store : a data structure abstraction library. In: 2020 IEEE/ACM Performance Modeling, Benchmarking and Simulation of High Performance Computer Systems (PMBS), Georgia, USA, 12 Nov 2020 pp. 71-85. ISBN 9781665422659. doi:10.1109/PMBS51919.2020.00013
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WRAP-Warwick-Data-Store-a-data-structure-abstraction-library-Kirk-2020.pdf - Accepted Version - Requires a PDF viewer. Download (977Kb) | Preview |
Official URL: https://doi.org/10.1109/PMBS51919.2020.00013
Abstract
With the increasing complexity of memory architectures and scientific applications, developing data structures that are performant, portable, scalable, and support developer productivity, is a challenging task. In this paper, we present Warwick Data Store (WDS), a lightweight and extensible C++ template library designed to manage these complexities and allow rapid prototyping. WDS is designed to abstract details of the underlying data structures away from the user, thus easing application development and optimisation. We show that using WDS does not significantly impact achieved performance across a variety of different scientific benchmarks and proxy-applications, compilers, and different architectures. The overheads are largely below 30% for smaller problems, with the overhead deceasing to below 10% when using larger problems. This shows that the library does not significantly impact the performance, while providing additional functionality to data structures, and the ability to optimise data structures without changing the application code.
Item Type: | Conference Item (Paper) | ||||||
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Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software | ||||||
Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | ||||||
Publisher: | IEEE | ||||||
ISBN: | 9781665422659 | ||||||
Official Date: | 1 January 2021 | ||||||
Dates: |
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Page Range: | pp. 71-85 | ||||||
DOI: | 10.1109/PMBS51919.2020.00013 | ||||||
Status: | Peer Reviewed | ||||||
Publication Status: | Published | ||||||
Re-use Statement: | © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | ||||||
Access rights to Published version: | Restricted or Subscription Access | ||||||
Copyright Holders: | Crown Copyright | ||||||
Date of first compliant deposit: | 27 April 2022 | ||||||
Date of first compliant Open Access: | 27 April 2022 | ||||||
Funder: | UK Atomic Weapons Establishment (AWE plc) | ||||||
Grant number: | CDK0724 | ||||||
Conference Paper Type: | Paper | ||||||
Title of Event: | 2020 IEEE/ACM Performance Modeling, Benchmarking and Simulation of High Performance Computer Systems (PMBS) | ||||||
Type of Event: | Workshop | ||||||
Location of Event: | Georgia, USA | ||||||
Date(s) of Event: | 12 Nov 2020 |
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