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A comparative study of multi-material data structures for computational physics applications

Research output: Contribution to journalArticle

  • Shane Fogerty
  • Matt Martineau
  • Rao Garimella
  • Robert Robey
Original languageEnglish
Pages (from-to)565-581
Number of pages17
JournalComputers and Mathematics with Applications
Volume78
Issue number2
Early online date28 Jun 2018
DOIs
DateAccepted/In press - 6 Jun 2018
DateE-pub ahead of print - 28 Jun 2018
DatePublished (current) - 15 Jul 2019

Abstract

The data structures used to represent the multi-material state of a computational physics application can have a significant impact on its performance. We look at efficient data structures for applications where there may be many materials overall, but where most computational cells contain only one or a few of these materials. We develop simple performance models for selecting possible data structures and programming patterns. We verify the analytic performance models with a small test program of the representative cases. We discuss the impact of these techniques and analysis for multi-material physics applications, which are also applicable to a wide range of sparse computational data structures.

    Research areas

  • Compact data structures, Compressed sparse data structures, Computational performance, Data structures, Multi-material physics

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