Approximating flow-sensitive pointer analysis using frequent itemset mining
Pages 225 - 234
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.
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- Approximating flow-sensitive pointer analysis using frequent itemset mining
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February 2015
280 pages
ISBN:9781479981618
- General Chairs:
- Kunle Olukotun,
- Aaron Smith,
- Program Chairs:
- Robert Hundt,
- Jason Mars
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- ACM: Association for Computing Machinery
- IEEE Computer Society TC-uARCH
- SIGMICRO: ACM Special Interest Group on Microarchitectural Research and Processing
- IEEE-CS\DATC: IEEE Computer Society
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Published: 07 February 2015
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CGO '15: 13th Annual IEEE/ACM International Symposium on Code Generation and Optimization
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