Abstract
This paper studies strategies for partitioning nested loop programs on distributed-memory multicomputers. A technique called skewed partition is studied. For certain applications the skewed partition will reduce the amount of synchronization and provide a greater control over the granularity than the commonly used block partition method. To illustrate the idea two examples, one on image distance transformation and another on solving systems of linear equations, are examined. Results obtained from a 32-node Ncube multicomputer show that the skewed partition improves the performance of these programs more than 50% over the block partition method. We will also discuss some shortcomings of skewed partition from the view point of practical implementation.