Heap decomposition for concurrent shape analysis

Roman Manevich; Tal Lev-Ami; Mooly Sagiv; Ganesan Ramalingam; Josh Berdine

doi:10.1007/978-3-540-69166-2_24

Heap decomposition for concurrent shape analysis

Roman Manevich
, Tal Lev-Ami
, Mooly Sagiv
, Ganesan Ramalingam
, Josh Berdine

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

21 Scopus citations

Abstract

We demonstrate shape analyses that can achieve a state space reduction exponential in the number of threads compared to the state-of-the-art analyses, while retaining sufficient precision to verify sophisticated properties such as linearizability. The key idea is to abstract the global heap by decomposing it into (not necessarily disjoint) subheaps, abstracting away some correlations between them. These new shape analyses are instances of an analysis framework based on heap decomposition. This framework allows rapid prototyping of complex static analyses by providing efficient abstract transformers given user-specified decomposition schemes. Initial experiments confirm the value of heap decomposition in scaling concurrent shape analyses.

Original language	English
Title of host publication	Static Analysis - 15th International Symposium, SAS 2008, Proceedings
Publisher	Springer Verlag
Pages	363-377
Number of pages	15
ISBN (Print)	3540691634, 9783540691631
DOIs	https://doi.org/10.1007/978-3-540-69166-2_24
State	Published - 1 Jan 2008
Externally published	Yes
Event	15th International Static Analysis Symposium, SAS 2008 - Valencia, Spain Duration: 16 Jul 2008 → 18 Jul 2008

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	5079 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	15th International Static Analysis Symposium, SAS 2008
Country/Territory	Spain
City	Valencia
Period	16/07/08 → 18/07/08

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 9 Industry, Innovation, and Infrastructure

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-540-69166-2_24

Cite this

Manevich, R., Lev-Ami, T., Sagiv, M., Ramalingam, G., & Berdine, J. (2008). Heap decomposition for concurrent shape analysis. In Static Analysis - 15th International Symposium, SAS 2008, Proceedings (pp. 363-377). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5079 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-540-69166-2_24

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title = "Heap decomposition for concurrent shape analysis",

abstract = "We demonstrate shape analyses that can achieve a state space reduction exponential in the number of threads compared to the state-of-the-art analyses, while retaining sufficient precision to verify sophisticated properties such as linearizability. The key idea is to abstract the global heap by decomposing it into (not necessarily disjoint) subheaps, abstracting away some correlations between them. These new shape analyses are instances of an analysis framework based on heap decomposition. This framework allows rapid prototyping of complex static analyses by providing efficient abstract transformers given user-specified decomposition schemes. Initial experiments confirm the value of heap decomposition in scaling concurrent shape analyses.",

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Manevich, R, Lev-Ami, T, Sagiv, M, Ramalingam, G & Berdine, J 2008, Heap decomposition for concurrent shape analysis. in Static Analysis - 15th International Symposium, SAS 2008, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 5079 LNCS, Springer Verlag, pp. 363-377, 15th International Static Analysis Symposium, SAS 2008, Valencia, Spain, 16/07/08. https://doi.org/10.1007/978-3-540-69166-2_24

Heap decomposition for concurrent shape analysis. / Manevich, Roman; Lev-Ami, Tal; Sagiv, Mooly et al.
Static Analysis - 15th International Symposium, SAS 2008, Proceedings. Springer Verlag, 2008. p. 363-377 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5079 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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N2 - We demonstrate shape analyses that can achieve a state space reduction exponential in the number of threads compared to the state-of-the-art analyses, while retaining sufficient precision to verify sophisticated properties such as linearizability. The key idea is to abstract the global heap by decomposing it into (not necessarily disjoint) subheaps, abstracting away some correlations between them. These new shape analyses are instances of an analysis framework based on heap decomposition. This framework allows rapid prototyping of complex static analyses by providing efficient abstract transformers given user-specified decomposition schemes. Initial experiments confirm the value of heap decomposition in scaling concurrent shape analyses.

AB - We demonstrate shape analyses that can achieve a state space reduction exponential in the number of threads compared to the state-of-the-art analyses, while retaining sufficient precision to verify sophisticated properties such as linearizability. The key idea is to abstract the global heap by decomposing it into (not necessarily disjoint) subheaps, abstracting away some correlations between them. These new shape analyses are instances of an analysis framework based on heap decomposition. This framework allows rapid prototyping of complex static analyses by providing efficient abstract transformers given user-specified decomposition schemes. Initial experiments confirm the value of heap decomposition in scaling concurrent shape analyses.

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Manevich R, Lev-Ami T, Sagiv M, Ramalingam G, Berdine J. Heap decomposition for concurrent shape analysis. In Static Analysis - 15th International Symposium, SAS 2008, Proceedings. Springer Verlag. 2008. p. 363-377. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-540-69166-2_24

Heap decomposition for concurrent shape analysis

Abstract

Publication series

Conference

UN SDGs

ASJC Scopus subject areas

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