Generating Representative Executions [Extended Abstract]
April 11, 2017 Β· Declared Dead Β· π PLACES@ETAPS
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Hendrik Maarand, Tarmo Uustalu
arXiv ID
1704.03098
Category
cs.PL: Programming Languages
Citations
1
Venue
PLACES@ETAPS
Last Checked
4 months ago
Abstract
Analyzing the behaviour of a concurrent program is made difficult by the number of possible executions. This problem can be alleviated by applying the theory of Mazurkiewicz traces to focus only on the canonical representatives of the equivalence classes of the possible executions of the program. This paper presents a generic framework that allows to specify the possible behaviours of the execution environment, and generate all Foata-normal executions of a program, for that environment, by discarding abnormal executions during the generation phase. The key ingredient of Mazurkiewicz trace theory, the dependency relation, is used in the framework in two roles: first, as part of the specification of which executions are allowed at all, and then as part of the normality checking algorithm, which is used to discard the abnormal executions. The framework is instantiated to the relaxed memory models of the SPARC hierarchy.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Programming Languages
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Tensor Comprehensions: Framework-Agnostic High-Performance Machine Learning Abstractions
R.I.P.
π»
Ghosted
Glow: Graph Lowering Compiler Techniques for Neural Networks
R.I.P.
π»
Ghosted
Learnable Programming: Blocks and Beyond
R.I.P.
π»
Ghosted
Scenic: A Language for Scenario Specification and Scene Generation
R.I.P.
π»
Ghosted
Vandal: A Scalable Security Analysis Framework for Smart Contracts
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted