Linearizability: a correctness condition for concurrent objects
ACM Transactions on Programming Languages and Systems (TOPLAS)
Simple, fast, and practical non-blocking and blocking concurrent queue algorithms
PODC '96 Proceedings of the fifteenth annual ACM symposium on Principles of distributed computing
Verifying safety properties of concurrent Java programs using 3-valued logic
POPL '01 Proceedings of the 28th ACM SIGPLAN-SIGACT symposium on Principles of programming languages
Parametric shape analysis via 3-valued logic
ACM Transactions on Programming Languages and Systems (TOPLAS)
Principles of Program Analysis
Principles of Program Analysis
TVLA: A System for Implementing Static Analyses
SAS '00 Proceedings of the 7th International Symposium on Static Analysis
Verifying safety properties using separation and heterogeneous abstractions
Proceedings of the ACM SIGPLAN 2004 conference on Programming language design and implementation
DCAS is not a silver bullet for nonblocking algorithm design
Proceedings of the sixteenth annual ACM symposium on Parallelism in algorithms and architectures
Region-based shape analysis with tracked locations
Proceedings of the 32nd ACM SIGPLAN-SIGACT symposium on Principles of programming languages
Proving correctness of highly-concurrent linearisable objects
Proceedings of the eleventh ACM SIGPLAN symposium on Principles and practice of parallel programming
Proceedings of the 2007 ACM SIGPLAN conference on Programming language design and implementation
Verifying Concurrent Data Structures by Simulation
Electronic Notes in Theoretical Computer Science (ENTCS)
Shape analysis by graph decomposition
TACAS'07 Proceedings of the 13th international conference on Tools and algorithms for the construction and analysis of systems
Comparison under abstraction for verifying linearizability
CAV'07 Proceedings of the 19th international conference on Computer aided verification
Scalable Shape Analysis for Systems Code
CAV '08 Proceedings of the 20th international conference on Computer Aided Verification
Thread Quantification for Concurrent Shape Analysis
CAV '08 Proceedings of the 20th international conference on Computer Aided Verification
Shape-Value Abstraction for Verifying Linearizability
VMCAI '09 Proceedings of the 10th International Conference on Verification, Model Checking, and Abstract Interpretation
Region Analysis for Race Detection
SAS '09 Proceedings of the 16th International Symposium on Static Analysis
Model Checking Linearizability via Refinement
FM '09 Proceedings of the 2nd World Congress on Formal Methods
Abstract Transformers for Thread Correlation Analysis
APLAS '09 Proceedings of the 7th Asian Symposium on Programming Languages and Systems
Context-aware counter abstraction
Formal Methods in System Design
Scalable automatic linearizability checking
Proceedings of the 33rd International Conference on Software Engineering
Shape analysis of low-level c with overlapping structures
VMCAI'10 Proceedings of the 11th international conference on Verification, Model Checking, and Abstract Interpretation
VMCAI'10 Proceedings of the 11th international conference on Verification, Model Checking, and Abstract Interpretation
Efficient bottom-up heap analysis for symbolic path-based data access summaries
Proceedings of the Tenth International Symposium on Code Generation and Optimization
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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.