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

Shane Fogerty*, Matt Martineau, Rao Garimella, Robert Robey

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

4 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)565-581
Number of pages17
JournalComputers and Mathematics with Applications
Issue number2
Early online date28 Jun 2018
Publication statusPublished - 15 Jul 2019


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


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