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Data partitioning and load balancing are important components of parallel computations. Many different partitioning strategies have been developed, with great effectiveness in parallel applications. But the load-balancing problem is not yet solved completely; new applications and architectures require new partitioning features. Existing algorithms must be(More)
Cluster and grid computing has made hierarchical and heterogeneous computing systems increasingly common as target environments for large-scale scientific computation. A cluster may consist of a network of multiprocessors. A grid computation may involve communication across slow interfaces. Modern supercomputers are often large clusters with hierarchical(More)
Modern parallel scientific computation is being performed in a wide variety of computational environments that include clusters, large-scale supercomputers, grid environments, and metacomputing environments. This presents challenges for application and library developers, who must develop architecture-aware software if they wish to utilize several computing(More)
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