Investigating autonomic runtime management strategies for SAMR applications

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

doi:10.1007/s10766-005-3589-z

Investigating autonomic runtime management strategies for SAMR applications

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

Electrical and Computer Engineering

Research output: Contribution to journal › Article › peer-review

4 Scopus citations

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 language	English (US)
Pages (from-to)	247-259
Number of pages	13
Journal	International Journal of Parallel Programming
Volume	33
Issue number	2-3
DOIs	https://doi.org/10.1007/s10766-005-3589-z
State	Published - Jun 2005

Keywords

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

ASJC Scopus subject areas

Software
Theoretical Computer Science
Information Systems

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10.1007/s10766-005-3589-z

Cite this

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title = "Investigating autonomic runtime management strategies for SAMR applications",

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.",

keywords = "Application/system sensitive reactive and proactive partitioning, GridARM autonomic runtime management framework, Structured adaptive mesh refinement",

author = "Sumir Chandra and Manish Parashar and Jingmei Yang and Yeliang Zhang and Salim Hariri",

note = "Funding Information: The research presented in this paper is supported in part by the National Science Foundation via grants numbers ACI 9984357, EIA 0103674, EIA 0120934, ANI 0335244, CNS 0305495, CNS 0426354, IIS 0430826 and by the DoE SciDAC program via grant number DE-FC02-01ER41184.",

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AU - Parashar, Manish

AU - Yang, Jingmei

AU - Zhang, Yeliang

AU - Hariri, Salim

N1 - Funding Information: The research presented in this paper is supported in part by the National Science Foundation via grants numbers ACI 9984357, EIA 0103674, EIA 0120934, ANI 0335244, CNS 0305495, CNS 0426354, IIS 0430826 and by the DoE SciDAC program via grant number DE-FC02-01ER41184.

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Investigating autonomic runtime management strategies for SAMR applications

Abstract

Keywords

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