TY - GEN
T1 - Thread-local semantics and its efficient sequential abstractions for race-free programs
AU - Mukherjee, Suvam
AU - Padon, Oded
AU - Shoham, Sharon
AU - D’Souza, Deepak
AU - Rinetzky, Noam
N1 - Publisher Copyright:
© 2017, Springer International Publishing AG.
PY - 2017
Y1 - 2017
N2 - 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].
AB - 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].
UR - http://www.scopus.com/inward/record.url?scp=85028664586&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-66706-5_13
DO - 10.1007/978-3-319-66706-5_13
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AN - SCOPUS:85028664586
SN - 9783319667058
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 253
EP - 276
BT - Static Analysis - 24th International Symposium, SAS 2017, Proceedings
A2 - Ranzato, Francesco
PB - Springer Verlag
T2 - 24th International Symposium on Static Analysis, SAS 2017
Y2 - 30 August 2017 through 1 September 2017
ER -