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European Symposium on Programming

ESOP 2020: Programming Languages and Systems pp 112–140Cite as

Optimal and Perfectly Parallel Algorithms for On-demand Data-Flow Analysis

Optimal and Perfectly Parallel Algorithms for On-demand Data-Flow Analysis

  • Krishnendu Chatterjee9,
  • Amir Kafshdar Goharshady9,
  • Rasmus Ibsen-Jensen10 &
  • …
  • Andreas Pavlogiannis11 
  • Conference paper
  • Open Access
  • First Online: 18 April 2020

  • 8170 Accesses

  • 5 Citations

Part of the Lecture Notes in Computer Science book series (LNTCS,volume 12075)

Abstract

Interprocedural data-flow analyses form an expressive and useful paradigm of numerous static analysis applications, such as live variables analysis, alias analysis and null pointers analysis. The most widely-used framework for interprocedural data-flow analysis is IFDS, which encompasses distributive data-flow functions over a finite domain. On-demand data-flow analyses restrict the focus of the analysis on specific program locations and data facts. This setting provides a natural split between (i) an offline (or preprocessing) phase, where the program is partially analyzed and analysis summaries are created, and (ii) an online (or query) phase, where analysis queries arrive on demand and the summaries are used to speed up answering queries.

In this work, we consider on-demand IFDS analyses where the queries concern program locations of the same procedure (aka same-context queries). We exploit the fact that flow graphs of programs have low treewidth to develop faster algorithms that are space and time optimal for many common data-flow analyses, in both the preprocessing and the query phase. We also use treewidth to develop query solutions that are embarrassingly parallelizable, i.e. the total work for answering each query is split to a number of threads such that each thread performs only a constant amount of work. Finally, we implement a static analyzer based on our algorithms, and perform a series of on-demand analysis experiments on standard benchmarks. Our experimental results show a drastic speed-up of the queries after only a lightweight preprocessing phase, which significantly outperforms existing techniques.

Keywords

  • Data-flow analysis
  • IFDS
  • Treewidth

The research was partly supported by Austrian Science Fund (FWF) Grant No. NFN S11407-N23 (RiSE/SHiNE), FWF Schrödinger Grant No. J-4220, Vienna Science and Technology Fund (WWTF) Project ICT15-003, Facebook PhD Fellowship Program, IBM PhD Fellowship Program, and DOC Fellowship No. 24956 of the Austrian Academy of Sciences (ÖAW). A longer version of this work is available at [17].

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Authors and Affiliations

  1. IST Austria, Klosterneuburg, Austria

    Krishnendu Chatterjee & Amir Kafshdar Goharshady

  2. University of Liverpool, Liverpool, UK

    Rasmus Ibsen-Jensen

  3. Aarhus University, Aarhus, Denmark

    Andreas Pavlogiannis

Authors
  1. Krishnendu Chatterjee
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  2. Amir Kafshdar Goharshady
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  3. Rasmus Ibsen-Jensen
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Correspondence to Amir Kafshdar Goharshady .

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  1. ETH Zurich, Zurich, Switzerland

    Prof. Dr. Peter Müller

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Chatterjee, K., Goharshady, A.K., Ibsen-Jensen, R., Pavlogiannis, A. (2020). Optimal and Perfectly Parallel Algorithms for On-demand Data-Flow Analysis. In: Müller, P. (eds) Programming Languages and Systems. ESOP 2020. Lecture Notes in Computer Science(), vol 12075. Springer, Cham. https://doi.org/10.1007/978-3-030-44914-8_5

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  • DOI: https://doi.org/10.1007/978-3-030-44914-8_5

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