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LCPC
1993
Springer

Maximizing Loop Parallelism and Improving Data Locality via Loop Fusion and Distribution

13 years 7 months ago
Maximizing Loop Parallelism and Improving Data Locality via Loop Fusion and Distribution
Abstract. Loop fusion is a program transformation that merges multiple loops into one. It is e ective for reducing the synchronization overhead of parallel loops and for improving data locality. This paper presents three results for fusion: (1) a new algorithm for fusing a collection of parallel and sequential loops, minimizing parallel loop synchronization while maximizing parallelism; (2) a proof that performing fusion to maximize data locality is NP-hard; and (3) two polynomial-time algorithms for improving data locality. These techniques also apply to loop distribution, which is shown to be essentially equivalent to loop fusion. Our approach is general enough to support other fusion heuristics. Preliminary experimental results validate our approach for improving performance by exploiting data locality and increasing the granularity of parallelism.
Ken Kennedy, Kathryn S. McKinley
Added 10 Aug 2010
Updated 10 Aug 2010
Type Conference
Year 1993
Where LCPC
Authors Ken Kennedy, Kathryn S. McKinley
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