Investigating autonomic runtime management strategies for SAMR applications

Authors:
Sumir Chandra;Manish Parashar;Jingmei Yang;Yeliang Zhang;Salim Hariri
Affiliations:
Department of Electrical and Computer Engineering, Rutgers University, Piscataway, NJ;Department of Electrical and Computer Engineering, Rutgers University, Piscataway, NJ;Department of Electrical and Computer Engineering, University of Arizona, Tucson, AZ;Department of Electrical and Computer Engineering, University of Arizona, Tucson, AZ;Department of Electrical and Computer Engineering, University of Arizona, Tucson, AZ
Venue:
International Journal of Parallel Programming - Special issue: The next generation software program
Year:
2005

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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.