A Programming Model for Semi-implicit Parallelization of Static Analyses
Parallelization of static analyses is necessary to scale to real-world programs, but it is a complex and difficult task and, therefore, often only done manually for selected high-profile analyses. In this paper, we propose a programming model for semi-implicit parallelization of static analyses which is inspired by reactive programming. Reusing the domain-expert knowledge on how to parallelize analyses encoded in the programming framework, developers do not need to think about parallelization and concurrency issues on their own. The programming model supports stateful computations, only requires monotonic computations over lattices, and is independent of specific analyses. Our evaluation shows the applicability of the programming model to different analyses and the importance of user-selected scheduling strategies. We implemented an IFDS solver that was able to outperform a state-of-the-art, specialized parallel IFDS solver both in absolute performance and scalability.
Wed 22 JulDisplayed time zone: Tijuana, Baja California change
12:10 - 13:10
STATIC ANALYSIS AND SEARCH-BASED TESTINGTechnical Papers at Zoom
Chair(s): Daniel Kroening University of Oxford
Public Live Stream/Recording. Registered participants should join via the Zoom link distributed in Slack.
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Asem Ghaleb , Karthik Pattabiraman University of British ColumbiaDOI Media Attached
|A Programming Model for Semi-implicit Parallelization of Static Analyses|
Dominik Helm TU Darmstadt, Germany, Florian Kübler TU Darmstadt, Germany, Jan Thomas Kölzer , Philipp Haller KTH Royal Institute of Technology, Michael Eichberg TU Darmstadt, Germany, Guido Salvaneschi Technische Universität Darmstadt, Mira Mezini Technische Universität DarmstadtDOI
|Recovering Fitness Gradients for Interprocedural Boolean Flags in Search-Based Testing|
Yun Lin National University of Singapore, Jun Sun Singapore Management University, Gordon Fraser University of Passau, Ziheng Xiu , Ting Liu Xi'an Jiaotong University, Jin Song Dong National University of SingaporeDOI Pre-print Media Attached