Proving Linearizability Using Reduction
June 21, 2018 Β· Declared Dead Β· π Computer/law journal
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Tangliu Wen
arXiv ID
1806.08206
Category
cs.PL: Programming Languages
Cross-listed
cs.DC,
cs.LO
Citations
1
Venue
Computer/law journal
Last Checked
4 months ago
Abstract
Lipton's reduction theory provides an intuitive and simple way for deducing the non-interference properties of concurrent programs, but it is difficult to directly apply the technique to verify linearizability of sophisticated fine-grained concurrent data structures. In this paper, we propose three reduction-based proof methods that can handle such data structures. The key idea behind our reduction methods is that an irreducible operation can be viewed as an atomic operation at a higher level of abstraction. This allows us to focus on the reduction properties of an operation related to its abstract semantics. We have successfully applied the methods to verify 11 concurrent data structures including the most challenging ones: the Herlihy and Wing queue, the HSY elimination-based stack, and the time-stamped queue, and the lazy list. Our methods inherit intuition and simplicity of Lipton's reduction, and concurrent data structures designers can easily and quickly learn to use the methods.
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