Approximating Flow-Sensitive Pointer Analysis Using Frequent Itemset Mining

Nagaraj, Vaivaswatha and Govindarajan, R (2015) Approximating Flow-Sensitive Pointer Analysis Using Frequent Itemset Mining. In: Proceedings of the IEEE/ACM International Symposium on Code Generation and Optimization (CGO), FEB 07-11, 2015, San Francisco, CA, pp. 225-234.

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Abstract

Pointer alias analysis is a well researched problem in the area of compilers and program verification. Many recent works in this area have focused on flow-sensitivity due to the additional precision it offers. However, a flow-sensitive analysis is computationally expensive, thus, preventing its use in larger programs. In this work, we observe that a number of object sets, consisting of tens to hundreds of objects appear together and frequently in many points-to sets. By approximating each of these object sets by a single object, we can speedup computation of points-to sets. Although the proposed approach incurs a slight loss in precision, it is shown to be safe. We use a well known data mining technique called frequent itemset mining to find these frequently occurring objects. We compare our approximation to a fully flow-sensitive pointer analysis on a set of ten benchmarks. We measure precision loss using two common client analysis queries and report an average precision loss of 0.25% on one measure and 1.40% on the other. The proposed approach results in a speedup of upto 12.9x (and an average speedup of 6.2x) in computing the points-to sets.

Item Type:	Conference Proceedings
Additional Information:	Copy right of this article belongs to the IEEE COMPUTER SOC, 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA
Department/Centre:	Division of Interdisciplinary Sciences > Supercomputer Education & Research Centre
Date Deposited:	08 Oct 2016 06:33
Last Modified:	08 Oct 2016 06:33
URI:	http://eprints.iisc.ac.in/id/eprint/54757

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