Investigating autonomic runtime management strategies for SAMR applications

Sumir Chandra, Manish Parashar, Jingmei Yang, Yeliang Zhang, Salim A Hariri

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

Dynamic structured adaptive mesh refinement (SAMR) techniques along with the emergence of the computational Grid offer the potential for realistic scientific and engineering simulations of complex physical phenomena. However, the inherent dynamic nature of SAMR applications coupled with the heterogeneity and dynamism of the underlying Grid environment present significant research challenges. This paper presents application/system sensitive reactive and proactive partitioning strategies that form a part of the GridARM autonomic runtime management framework. An evaluation using different SAMR kernels and system workloads is presented to demonstrate the improvement in overall application performance.

Original languageEnglish (US)
Pages (from-to)247-259
Number of pages13
JournalInternational Journal of Parallel Programming
Volume33
Issue number2-3
DOIs
StatePublished - Jun 2005

Fingerprint

Adaptive Mesh Refinement
Computational Grid
Workload
Partitioning
kernel
Grid
Engineering
Evaluation
Demonstrate
Strategy
Simulation

Keywords

  • Application/system sensitive reactive and proactive partitioning
  • GridARM autonomic runtime management framework
  • Structured adaptive mesh refinement

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computational Theory and Mathematics

Cite this

Investigating autonomic runtime management strategies for SAMR applications. / Chandra, Sumir; Parashar, Manish; Yang, Jingmei; Zhang, Yeliang; Hariri, Salim A.

In: International Journal of Parallel Programming, Vol. 33, No. 2-3, 06.2005, p. 247-259.

Research output: Contribution to journalArticle

Chandra, Sumir ; Parashar, Manish ; Yang, Jingmei ; Zhang, Yeliang ; Hariri, Salim A. / Investigating autonomic runtime management strategies for SAMR applications. In: International Journal of Parallel Programming. 2005 ; Vol. 33, No. 2-3. pp. 247-259.
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