Improving Locality for Adaptive Irregular Scientific Codes

Irregular scientific codes experience poor cache performance due to their memory access patterns. In this paper, we examine two issues for locality optimizations for irregular computations. First, we experimentally find locality optimization can improve performance for parallel codes, but is dependent on the parallelization techniques used. Second, we show locality optimization may be used to improve performance even for adaptive codes. We develop a cost model which can be employed to calculate an efficient optimization frequency; it may be applied dynamically instrumenting the program to measure execution time per time-step iteration. Our results are validated through experiments on three representative irregular scientific codes.

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