Enhancing The Parallelization Of Sparse Matrices Through Dynamic Issues

This work presents a survey of the capabilities that the sparse computation offers for improving performance when parallelized, either automatically or through a data-parallel compiler. The characterization of a sparse code gets more complicated as code length increases: Access patterns change from loop to loop, thus making necessary to redefine the parallelization strategy. While dense computation solely offers the possibility of redistributing data structures, several of her factors influence the performance of a code excerpt in the sparse field like source data representation on file, compressed data storage in. memory, the fill-in feature and the number of processors required by the execution. We analize the alternatives that arise from, each issue, providing a guideline for the underlying compilation. work and illustrating our techniques with examples on the Gray T3E.