Thread-local semantics and its efficient sequential abstractions for race-free programs

Suvam Mukherjee*, Oded Padon, Sharon Shoham, Deepak D’Souza, Noam Rinetzky

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations


Data race free (DRF) programs constitute an important class of concurrent programs. In this paper we provide a framework for designing and proving the correctness of data flow analyses that target this class of programs, and which are in the same spirit as the “sync-CFG” analysis originally proposed in [9]. To achieve this, we first propose a novel concrete semantics for DRF programs called L-DRF that is thread-local in nature with each thread operating on its own copy of the data state. We show that abstractions of our semantics allow us to reduce the analysis of DRF programs to a sequential analysis. This aids in rapidly porting existing sequential analyses to scalable analyses for DRF programs. Next, we parameterize the semantics with a partitioning of the program variables into “regions” which are accessed atomically. Abstractions of the region-parameterized semantics yield more precise analyses for region-race free concurrent programs. We instantiate these abstractions to devise efficient relational analyses for race free programs, which we have implemented in a prototype tool called RATCOP. On the benchmarks, RATCOP was able to prove upto 65% of the assertions, in comparison to 25% proved by a version of the analysis from [9].

Original languageEnglish
Title of host publicationStatic Analysis - 24th International Symposium, SAS 2017, Proceedings
EditorsFrancesco Ranzato
PublisherSpringer Verlag
Number of pages24
ISBN (Print)9783319667058
StatePublished - 2017
Event24th International Symposium on Static Analysis, SAS 2017 - New York, United States
Duration: 30 Aug 20171 Sep 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10422 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference24th International Symposium on Static Analysis, SAS 2017
Country/TerritoryUnited States
CityNew York


FundersFunder number
Blavat-nik Family Foundation
European Research Council321174
Tel Aviv University
Seventh Framework Programme


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