Abstraction-guided synthesis of synchronization

VechevMartin,; YahavEran,; YorshGreta,

doi:10.1145/1707801.1706338

Cited by 64 publications

(67 citation statements)

References 25 publications

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“…The focus of this work, and the "heart" of our algorithm is therefore step 3. The general approach we use (similarly to the work of Vechev et al [35]) is not to try to compute fence locations directly, but use an intermediate constraint language.…”

Section: Constraint Generationmentioning

confidence: 99%

“…One possible approach, in the spirit of previous work by Vechev et al [35], is to enumerate all (acyclic) traces leading to an error state and try to prevent each trace by enforcing appropriate constraints. However, such enumeration does not scale to practical programs as the number of traces can be exponential in the size of the transition system which is itself exponential in program length.…”

Section: Acm Sigact Newsmentioning

confidence: 99%

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Automatic inference of memory fences

2012

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We addresses the problem of automatic verification and fence inference in concurrent programs running under relaxed memory models. Modern architectures implement relaxed memory models in which memory operations may be reordered and executed non-atomically. Instructions called memory fences are provided to the programmer, allowing control of this behavior. To ensure correctness of many algorithms, the programmer is often required to explicitly insert memory fences into her program. However, she must use as few fences as possible, or the benefits of the relaxed architecture may be lost. It is our goal to help automate the fence insertion process.We present an algorithm for automatic inference of memory fences in concurrent programs, relieving the programmer from this complex task. Given a finite-state program, a safety specification and a description of the memory model our algorithm computes a set of ordering constraints that guarantee the correctness of the program under the memory model. The computed constraints are maximally permissive: removing any constraint from the solution would permit an execution violating the specification. These constraints are then realized as additional fences in the input program.We implemented our approach in a pair of tools called fender and blender and used them to infer correct and efficient placements of fences for several non-trivial algorithms, including practical mutual exclusion primitives and concurrent data structures.

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Section: Constraint Generationmentioning

confidence: 99%

Section: Acm Sigact Newsmentioning

confidence: 99%

Automatic inference of memory fences

2012

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“…This suggests that a procedure for quantitative synthesis should aim to meet its boolean and quantitative objectives simultaneously. The benefits of such an approach are corroborated by prior work on (boolean) synthesis [28,34], which combines synthesis and verification to make the overall process more tractable. In this paper, we offer such a combined verification and synthesis procedure for the problem of synthesizing values for unknown program parameters.…”

Section: Introductionmentioning

confidence: 92%

Bridging boolean and quantitative synthesis using smoothed proof search

Chaudhuri

Clochard

Solar-Lezama

2014

Proceedings of the 41st ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages

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We present a new technique for parameter synthesis under boolean and quantitative objectives. The input to the technique is a "sketch" -a program with missing numerical parameters -and a probabilistic assumption about the program's inputs. The goal is to automatically synthesize values for the parameters such that the resulting program satisfies: (1) a boolean specification, which states that the program must meet certain assertions, and (2) a quantitative specification, which assigns a real valued rating to every program and which the synthesizer is expected to optimize.Our method -called smoothed proof search -reduces this task to a sequence of unconstrained smooth optimization problems that are then solved numerically. By iteratively solving these problems, we obtain parameter values that get closer and closer to meeting the boolean specification; at the limit, we obtain values that provably meet the specification. The approximations are computed using a new notion of smoothing for program abstractions, where an abstract transformer is approximated by a function that is continuous according to a metric over abstract states.We present a prototype implementation of our synthesis procedure, and experimental results on two benchmarks from the embedded control domain. The experiments demonstrate the benefits of smoothed proof search over an approach that does not meet the boolean and quantitative synthesis goals simultaneously.

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“…Software synthesis is an active area of research [6,17,19,22]. It has received increased attention recently, but has been studied for decades [3,11,12,16].…”

Section: Introductionmentioning

confidence: 99%

Reductions for Synthesis Procedures

Jacobs

Kunčak

Suter

2013

Lecture Notes in Computer Science

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Abstract. A synthesis procedure acts as a compiler for declarative specifications. It accepts a formula describing a relation between inputs and outputs, and generates a function implementing this relation. This paper presents the first synthesis procedures for 1) algebraic data types and 2) arrays. Our procedures are reductions that lift a synthesis procedure for the elements into synthesis procedures for containers storing these elements. We introduce a framework to describe synthesis procedures as systematic applications of inference rules. We show that, by interpreting both synthesis problems and programs as relations, we can derive and modularly prove widely applicable transformation rules, simplifying both the presentation and the correctness argument.

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Abstraction-guided synthesis of synchronization

Cited by 64 publications

References 25 publications

Automatic inference of memory fences

Automatic inference of memory fences

Bridging boolean and quantitative synthesis using smoothed proof search

Reductions for Synthesis Procedures

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