Specifying and Verifying Persistent Libraries
June 02, 2023 Β· Declared Dead Β· π European Symposium on Programming
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
LΓ©o Stefanesco, Azalea Raad, Viktor Vafeiadis
arXiv ID
2306.01614
Category
cs.PL: Programming Languages
Citations
1
Venue
European Symposium on Programming
Last Checked
4 months ago
Abstract
We present a general framework for specifying and verifying persistent libraries, that is, libraries of data structures that provide some persistency guarantees upon a failure of the machine they are executing on. Our framework enables modular reasoning about the correctness of individual libraries (horizontal and vertical compositionality) and is general enough to encompass all existing persistent library specifications ranging from hardware architectural specifications to correctness conditions such as durable linearizability. As case studies, we specify the FliT and Mirror libraries, verify their implementations over Px86, and use them to build higher-level durably linearizable libraries, all within our framework. We also specify and verify a persistent transaction library that highlights some of the technical challenges which are specific to persistent memory compared to weak memory and how they are handled by our framework.
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