Abstract

Two-dimensional arrays are useful in a large variety of scientific and engineering applications. Parallelization of these applications requires the decomposition of array elements among different machines. Several data-decomposition techniques have been studied in the literature for machines with uniform computational power. In this paper we develop new methods for decomposing arrays into a cluster of machines with nonuniform computational power. Simulation results show that our methods provide superior decomposition over naive schemes. 1 Introduction Data-parallel applications requires the partitioning of data among processors in a way that the computation load on each node is proportional to its computational power, while minimizing communication. Two-dimensional arrays are widely used in scientific and engineering problems such as weather prediction and image processing. In this paper we discuss the decomposition of twodimensional arrays for a nonuniform computational environment ...

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