By the same authors

Warwick Data Store: A Data Structure Abstraction Library

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Published copy (DOI)


  • Richard O. Kirk
  • Martin Nolten
  • Robert Kevis
  • Timothy R. Law
  • Satheesh Maheswaran
  • Steven A. Wright
  • Seimon Powell
  • Gihan R. Mudalige
  • Stephen A. Jarvis


Publication details

Title of host publicationProceedings of PMBS 2020
DatePublished - Nov 2020
Number of pages15
PublisherInstitute of Electrical and Electronics Engineers Inc.
Original languageEnglish
ISBN (Electronic)9780738110486

Publication series

NameProceedings of PMBS 2020: Performance Modeling, Benchmarking and Simulation of High Performance Computer Systems, Held in conjunction with SC 2020: The International Conference for High Performance Computing, Networking, Storage and Analysis


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.

Bibliographical note

Publisher Copyright:
© 2020 IEEE.

Copyright 2021 Elsevier B.V., All rights reserved.

    Research areas

  • Data Structures, High Performance Computing, Mini-Applications

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