Effective abstractions for verification under relaxed memory models

Dan, Andrei; Meshman, Yuri; Vechev, Martin; Yahav, Eran

doi:10.1016/j.cl.2016.02.003

Cited by 15 publications

(18 citation statements)

References 26 publications

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“…Using this fact, (X, X ) ∈ G * given by (18) and the definition of G given by (17), we can show that (σ, σ ) = (evalF (X), evalF (X )) ∈ (G0 ∪ G((X .tok(e)) ⊥ )) * = (G0 ∪ G((F tok o (σ)) ⊥ )) * , thus establishing (19). Then the conclusion of S3 yields…”

Section: Propositionmentioning

confidence: 72%

“…These papers thus explore verification approaches different from the one considered in this paper. Additionally, most of the papers have focused on models similar to TSO [3,13,18,19], which is stronger than the causal consistency model we consider as a baseline. As the target correctness property, papers on weak memory models have often considered robustness (see above), which is too strong a requirement for our setting.…”

Section: Related Workmentioning

confidence: 99%

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'Cause I'm strong enough: Reasoning about consistency choices in distributed systems

et al. 2016

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Large-scale distributed systems often rely on replicated databases that allow a programmer to request different data consistency guarantees for different operations, and thereby control their performance. Using such databases is far from trivial: requesting stronger consistency in too many places may hurt performance, and requesting it in too few places may violate correctness. To help programmers in this task, we propose the first proof rule for establishing that a particular choice of consistency guarantees for various operations on a replicated database is enough to ensure the preservation of a given data integrity invariant. Our rule is modular: it allows reasoning about the behaviour of every operation separately under some assumption on the behaviour of other operations. This leads to simple reasoning, which we have automated in an SMT-based tool. We present a nontrivial proof of soundness of our rule and illustrate its use on several examples.

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Section: Propositionmentioning

confidence: 72%

Section: Related Workmentioning

confidence: 99%

'Cause I'm strong enough: Reasoning about consistency choices in distributed systems

et al. 2016

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show abstract

“…This is in contrast to previous methods exposing the store buffers in the program states (e.g. (Dan et al 2015)) or explicitly considering all possible reshuffles e.g. by program transformation (e.g.…”

Section: Related Work On Invariance Proof For Wcmmentioning

confidence: 90%

Ogre and Pythia: an invariance proof method for weak consistency models

AlglaveJade

CousotPatrick

2017

SIGPLAN Not.

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We design an invariance proof method for concurrent programs parameterised by a weak consistency model. The calculational design of the invariance proof method is by abstract interpretation of a truly parallel analytic semantics. This generalises the methods by Lamport and Owicki-Gries for sequential consistency. We use cat as an example of language to write consistency specifications of both concurrent programs and machine architectures.

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“…Alglave et al [6] address this problem for PowerPC with a specification of the platform's barrier semantics, including cumulativity; we automatically synthesize this specification in Section 6.1. Another common application is verification of concurrent code under relaxed memory models, and several tools have been developed for this purpose (e.g., [18,20]). All of them rely on formal specifications of memory models that can be synthesized with MemSynth.…”

Section: Related Workmentioning

confidence: 99%

Synthesizing memory models from framework sketches and Litmus tests

Bornholt

Torlak

2017

SIGPLAN Not.

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A memory consistency model specifies which writes to shared memory a given read may see. Ambiguities or errors in these specifications can lead to bugs in both compilers and applications. Yet architectures usually define their memory models with prose and litmus tests-small concurrent programs that demonstrate allowed and forbidden outcomes. Recent work has formalized the memory models of common architectures through substantial manual effort, but as new architectures emerge, there is a growing need for tools to aid these efforts. This paper presents MemSynth, a synthesis-aided system for reasoning about axiomatic specifications of memory models. MemSynth takes as input a set of litmus tests and a framework sketch that defines a class of memory models. The sketch comprises a set of axioms with missing expressions (or holes). Given these inputs, MemSynth synthesizes a completion of the axioms-i.e., a memory model-that gives the desired outcome on all tests. The MemSynth engine employs a novel embedding of bounded relational logic in a solver-aided programming language, which enables it to tackle complex synthesis queries intractable to existing relational solvers. This design also enables it to solve new kinds of queries, such as checking if a set of litmus tests unambiguously defines a memory model within a framework sketch. We show that MemSynth can synthesize specifications for x86 in under two seconds, and for PowerPC in 12 seconds from 768 litmus tests. Our ambiguity check identifies missing tests from both the Intel x86 documentation and the validation suite of a previous PowerPC formalization. We also used MemSynth to reproduce, debug, and automatically repair a paper on comparing memory models in just two days. CCS Concepts • Computer systems organization → Parallel architectures; • Software and its engineering → Automatic programming

show abstract

Effective abstractions for verification under relaxed memory models

Cited by 15 publications

References 26 publications

'Cause I'm strong enough: Reasoning about consistency choices in distributed systems

'Cause I'm strong enough: Reasoning about consistency choices in distributed systems

Ogre and Pythia: an invariance proof method for weak consistency models

Synthesizing memory models from framework sketches and Litmus tests

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