Investigating autonomic runtime management strategies for SAMR applications

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

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


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
Issue number2-3
StatePublished - Jun 2005

All Science Journal Classification (ASJC) codes

  • Software
  • Theoretical Computer Science
  • Information Systems


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


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