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Title: A Comparative Study of Multi-material Data Structures for Computational Physics Applications

Abstract

The data structures used to represent the multi-material state of a computational physics application can have a drastic impact on the performance of the application. We look at efficient data structures for sparse applications where there may be many materials, but only one or few in most computational cells. We develop simple performance models for use in selecting possible data structures and programming patterns. We verify the analytic models of performance through a small test program of the representative cases.

Authors:
 [1];  [1]
  1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Publication Date:
Research Org.:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1341844
Report Number(s):
LA-UR-16-23889
DOE Contract Number:  
AC52-06NA25396
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; Multi-material data structures; performance models; compact data storage

Citation Formats

Garimella, Rao Veerabhadra, and Robey, Robert W. A Comparative Study of Multi-material Data Structures for Computational Physics Applications. United States: N. p., 2017. Web. doi:10.2172/1341844.
Garimella, Rao Veerabhadra, & Robey, Robert W. A Comparative Study of Multi-material Data Structures for Computational Physics Applications. United States. doi:10.2172/1341844.
Garimella, Rao Veerabhadra, and Robey, Robert W. Tue . "A Comparative Study of Multi-material Data Structures for Computational Physics Applications". United States. doi:10.2172/1341844. https://www.osti.gov/servlets/purl/1341844.
@article{osti_1341844,
title = {A Comparative Study of Multi-material Data Structures for Computational Physics Applications},
author = {Garimella, Rao Veerabhadra and Robey, Robert W.},
abstractNote = {The data structures used to represent the multi-material state of a computational physics application can have a drastic impact on the performance of the application. We look at efficient data structures for sparse applications where there may be many materials, but only one or few in most computational cells. We develop simple performance models for use in selecting possible data structures and programming patterns. We verify the analytic models of performance through a small test program of the representative cases.},
doi = {10.2172/1341844},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Tue Jan 31 00:00:00 EST 2017},
month = {Tue Jan 31 00:00:00 EST 2017}
}

Technical Report:

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