In this paper, a methodology for mapping algorithms onto heterogeneous suite of supercomputers is presented. An approach for selecting an optimal suite of computers for solving problems with diverse computational requirements, called Heterogeneous Optimal Selection Theory (HOST), is presented. HOST is an extension to Augmented Optimal Selection Theory in two ways: It incorporates heterogeneous parallelism embedded in the tasks, and it reflects the costs associated in using various fine grain mapping strategies at individual machine level. The proposed mapping methodology is based on the Cluster-M programming paradigm. For the mapping purpose, the input format, assumed in HOST, is modeled in terms of Hierarchical Cluster-M specification and representation. For a given problem, a Hierarchical Cluster-M specification is generated to indicate the execution of concurrent tasks at different stages of the computation. This specification is then mapped onto the Hierarchical Cluster- M representation of the underlying heterogeneous suite of supercomputers.

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Note: imported from RIT’s Digital Media Library running on DSpace to RIT Scholar Works in February 2014.

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Department, Program, or Center

Computer Engineering (KGCOE)


RIT – Main Campus