Parallelization Techniques for Sparse Matrix Applications

Sparse matrix problems are difficult to parallelize efficiently on distributed memory machines since data is often accessed indirectly. Inspector/executor strategies, which are typically used to parallelize loops with indirect references, incur substantial runtime preprocessing overheads when references with multiple levels of indirection are encountered --- a frequent occurrence in sparse matrix algorithms. The sparse array rolling (SAR) technique, introduced in [15], significantly reduces these preprocessing overheads. This paper outlines the SAR approach and describes its runtime support accompanied by a detailed performance evaluation. The results demonstrate that SAR yields significant reduction in preprocessing overheads compared to standard inspector/executor techniques. 1 Introduction Sparse matrices are used in a large number of important scientific codes, such as molecular dynamics, CFD solvers, finite element methods and climate modelling. Unfortunately, these applications..