Abstract

In this paper we present a framework for partitioning data parallel computations across a heterogeneous metasystem at runtime. The framework is guided by program and resource information which are made available to the system. Three difficult problems are handled by the framework: processor selection, task placement, and heterogeneous data domain decomposition. Solving each of these problems contributes to reduced elapsed time. In particular, processor selection determines the best grain size at which to run the computation, task placement reduces communication cost, and data domain decomposition achieves processor load balance. We present results that indicate excellent performance is achievable using the framework. This paper extends our earlier work in partitioning data parallel computations across a single-level network of heterogeneous workstations 1 . 1.0 Introduction A great deal of recent interest has been sparked within academic, industry, and government circles in the emer...

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