Balancing Processor Loads and Exploiting Data Locality in N-Body Simulations

Abstract

Although N-body simulation algorithms are amenable to parallelization, performance gains from execution on parallel machines are difficult to obtain due to load imbalances caused by irregular distributions of bodies. In general, there is a tension between balancing processor loads and maintaining locality, as the dynamic re-assignment of work necessitates access to remote data. Fractiling is a dynamic scheduling scheme that simultaneously balances processor loads and maintains locality by exploiting the self-similarity properties of fractals. Fractiling is based on a probabilistic analysis, and thus, accommodates load imbalances caused by predictable phenomena, such as irregular data, and unpredictable phenomena, such as data-access latencies. In experiments on a KSR1, performance of N-body simulation codes were improved by as much as 53% by fractiling. Performance improvements were obtained on uniform and nonuniform distributions of bodies, underscoring the need for a scheduling scheme that accommodates system induced variance. As the fractiling scheme is orthogonal to the N-body algorithm, we could use simple codes that discretize space into equal-size subrectangles (2-d) or subcubes (3-d) as the base algorithms.