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Proceedings of the 2013 IEEE/ACM International Symposium on Code Generation and Optimization (CGO) (2009)
Seattle, Washington
Mar. 22, 2009 to Mar. 25, 2009
ISBN: 978-0-7695-3576-0
pp: 126-135
ABSTRACT
This paper describes two new algorithms for solving inclusion based points-to analysis. The first algorithm, the Wave Propagation Method, is a modified version of an early technique presented by Pearce et al; however, it greatly improves on the running time of its predecessor. The second algorithm, the Deep Propagation Method, is a more light-weighted analysis, that requires less memory. We have compared these algorithms with three state-of-the-art techniques by Hardekopf-Lin, Heintze-Tardieu and Pearce-Kelly-Hankin. Our experiments show that Deep Propagation has the best average execution time across a suite of 17 well-known benchmarks, the lowest memory requirements in absolute numbers, and the fastest absolute times for benchmarks under 100,000 lines of code. The memory-hungry Wave Propagation has the fastest absolute running times in a memory rich execution environment, matching the speed of the best known points-to analysis algorithms in large benchmarks.
INDEX TERMS
Pointer analysis, Inclusion based, Context insensitive, Cycle detection
CITATION
Fernando Magno Quintao Pereira, Daniel Berlin, "Wave Propagation and Deep Propagation for Pointer Analysis", Proceedings of the 2013 IEEE/ACM International Symposium on Code Generation and Optimization (CGO), vol. 00, no. , pp. 126-135, 2009, doi:10.1109/CGO.2009.9
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