Dynamic load partitioning strategies for managing data of space and time heterogeneity in parallel SAMR applications

Xiaolin Li, Manish Parashar

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

This paper presents the design and experimental evaluation of two dynamic load partitioning and balancing strategies for parallel Structured Adaptive Mesh Refinement (SAMR) applications: the Level-based Partitioning Algorithm (LPA) and the Hierarchical Partitioning Algorithm (HPA). These techniques specifically address the computational and communication heterogeneity across refinement levels of the adaptive grid hierarchy underlying these methods. An experimental evaluation of the partitioning schemes is also presented.

Original languageEnglish (US)
Pages (from-to)181-188
Number of pages8
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2790
StatePublished - 2004

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Keywords

  • Dynamic Load Balancing
  • Parallel and Distributed Computing
  • Scientific Computing
  • Structured Adaptive Mesh Refinement

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