A comparative study of multi-material data structures for computational physics applications. (15th July 2019)

• Record Type: Journal Article
• Authors: Fogerty, Shane; Martineau, Matt; Garimella, Rao; Robey, Robert
• Is Part Of: Computers & mathematics with applications. Volume 78:issue 2(2019)
• Page Start: 565
• Page End: 581
• Publication Date: 2019-07-15
• Subjects: Multi-material physics -- Data structures -- Computational performance -- Compressed sparse data structures -- Compact data structures; Electronic data processing -- Periodicals; Mathematics -- Data processing -- Periodicals; 510.28541
• DOI: 10.1016/j.camwa.2018.06.010
• Languages: English
• ISSNs: 0898-1221

Abstract

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.