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Abstract:

Irregular scientific codes experience poor cache performance due to their memory access patterns. We examine several data and computation locality transformations including GPART, a new technique based on hierarchical clustering. GPART constructs quality partitions quickly by clustering multiple neighboring nodes in a few passes, with priority on nodes with high degree. Overhead is kept low by considering only edges between partitions. We develop compiler analyses and transformations in SUIF to automatically apply locality transformations, and propose user annotations to locate coordinate information needed by geometric partitioning algorithms. We experimentally evaluate locality optimizations for both static and adaptive codes, where connection patterns dynamically change at intervals during program execution. We derive a simple cost model to guide locality optimizations when access patterns change. Experiments on several irregular scientific codes show locality optimization techniques are effective improving performance by 30--50 % on average. In particular, GPART closely matches the performance of more sophisticated partitioning algorithms, with one third of the overhead.

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