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Eigenvalue Grid and Cluster Computations, Using Task Farming Computing Paradigm and Data Persistency

By Serge Petiton and Laurent Choy

Abstract

International audienceRecent progress has made possible to construct high performance distributed computing environments, such as computational grids and cluster of clusters, which provide access to large scale heterogeneous computational resources. Exploration of novel algorithms and evaluation of performance is a strategy research for the future of computational grid and cluster scientific computing for many important applications. We adapted the well-known parallel iterative Lanczos method to compute Hermitian eigenvalues of large sparse matrices for a GRID platform and for a cluster of clusters worldwide deployed between France and Japan. Parts of the proposed GRID algorithm use an efficient task-farming computing paradigm, with data persistency scheduling strategies

Topics: [INFO.INFO-OH]Computer Science [cs]/Other [cs.OH]
Publisher: HAL CCSD
Year: 2007
OAI identifier: oai:HAL:hal-00694501v1
Provided by: HAL - Lille 3
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