Abstract
In this paper we first present several compiler techniques to reduce the overhead of run-time parallelization. We show how to use static control flow information to reduce the number of memory references that need to be traced at run-time. Then we introduce several methods designed specifically for the parallelization of sparse applications. We detail some heuristics on how to speculate on the type and data structures used by the original code and thus reduce the memory requirements for tracing the sparse access patterns without performing any additional work. Optimization techniques for the sparse reduction parallelization and speculative loop distribution conclude the paper.
A full version of this paper is available as Technical Report TR99-025, Dept. of Computer Science, Texas A&M University
Research supported in part by NSF CAREER Award CCR-9734471, NSF Grant ACI-9872126, DOE ASCI ASAP Level 2 Grant B347886 and a Hewlett-Packard Equipment Grant
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Yu, H., Rauchwerger, L. (2000). Run-Time Parallelization Optimization Techniques. In: Carter, L., Ferrante, J. (eds) Languages and Compilers for Parallel Computing. LCPC 1999. Lecture Notes in Computer Science, vol 1863. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44905-1_36
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DOI: https://doi.org/10.1007/3-540-44905-1_36
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