A Selection Theory and Methodology for Heterogeneous Supercomputing

In this paper, a methodology for mapping algorithms onto heterogeneous suite of supercomputers is presented. A n 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 t o 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 hj'ierarchical Cluster-M specification is generated t o indicate the execution of concurrent tasks at different stizges of the computation. This specification is then mapped onto the Hierarchical ClusterM representation ofthe underlying heterogeneous suite of supercomputers.