Pub Date : 2023-08-02DOI: 10.1007/978-3-031-45332-8_10
D. Thanos, T. Coopmans, A. Laarman
{"title":"Fast Equivalence Checking of Quantum Circuits of Clifford Gates","authors":"D. Thanos, T. Coopmans, A. Laarman","doi":"10.1007/978-3-031-45332-8_10","DOIUrl":"https://doi.org/10.1007/978-3-031-45332-8_10","url":null,"abstract":"","PeriodicalId":335085,"journal":{"name":"Automated Technology for Verification and Analysis","volume":"63 1","pages":"199-216"},"PeriodicalIF":0.0,"publicationDate":"2023-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139352069","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-29DOI: 10.48550/arXiv.2307.15907
Ye Tao, Wanwei Liu, Fu Song, Zhen Liang, J. Wang, Hongxu Zhu
Deep neural networks, (DNNs, a.k.a. NNs), have been widely used in various tasks and have been proven to be successful. However, the accompanied expensive computing and storage costs make the deployments in resource-constrained devices a significant concern. To solve this issue, quantization has emerged as an effective way to reduce the costs of DNNs with little accuracy degradation by quantizing floating-point numbers to low-width fixed-point representations. Quantized neural networks (QNNs) have been developed, with binarized neural networks (BNNs) restricted to binary values as a special case. Another concern about neural networks is their vulnerability and lack of interpretability. Despite the active research on trustworthy of DNNs, few approaches have been proposed to QNNs. To this end, this paper presents an automata-theoretic approach to synthesizing BNNs that meet designated properties. More specifically, we define a temporal logic, called BLTL, as the specification language. We show that each BLTL formula can be transformed into an automaton on finite words. To deal with the state-explosion problem, we provide a tableau-based approach in real implementation. For the synthesis procedure, we utilize SMT solvers to detect the existence of a model (i.e., a BNN) in the construction process. Notably, synthesis provides a way to determine the hyper-parameters of the network before training.Moreover, we experimentally evaluate our approach and demonstrate its effectiveness in improving the individual fairness and local robustness of BNNs while maintaining accuracy to a great extent.
{"title":"An Automata-Theoretic Approach to Synthesizing Binarized Neural Networks","authors":"Ye Tao, Wanwei Liu, Fu Song, Zhen Liang, J. Wang, Hongxu Zhu","doi":"10.48550/arXiv.2307.15907","DOIUrl":"https://doi.org/10.48550/arXiv.2307.15907","url":null,"abstract":"Deep neural networks, (DNNs, a.k.a. NNs), have been widely used in various tasks and have been proven to be successful. However, the accompanied expensive computing and storage costs make the deployments in resource-constrained devices a significant concern. To solve this issue, quantization has emerged as an effective way to reduce the costs of DNNs with little accuracy degradation by quantizing floating-point numbers to low-width fixed-point representations. Quantized neural networks (QNNs) have been developed, with binarized neural networks (BNNs) restricted to binary values as a special case. Another concern about neural networks is their vulnerability and lack of interpretability. Despite the active research on trustworthy of DNNs, few approaches have been proposed to QNNs. To this end, this paper presents an automata-theoretic approach to synthesizing BNNs that meet designated properties. More specifically, we define a temporal logic, called BLTL, as the specification language. We show that each BLTL formula can be transformed into an automaton on finite words. To deal with the state-explosion problem, we provide a tableau-based approach in real implementation. For the synthesis procedure, we utilize SMT solvers to detect the existence of a model (i.e., a BNN) in the construction process. Notably, synthesis provides a way to determine the hyper-parameters of the network before training.Moreover, we experimentally evaluate our approach and demonstrate its effectiveness in improving the individual fairness and local robustness of BNNs while maintaining accuracy to a great extent.","PeriodicalId":335085,"journal":{"name":"Automated Technology for Verification and Analysis","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125254685","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-20DOI: 10.48550/arXiv.2307.10891
Calvin Chau, Jan Křetínský, S. Mohr
Abstraction is a key verification technique to improve scalability. However, its use for neural networks is so far extremely limited. Previous approaches for abstracting classification networks replace several neurons with one of them that is similar enough. We can classify the similarity as defined either syntactically (using quantities on the connections between neurons) or semantically (on the activation values of neurons for various inputs). Unfortunately, the previous approaches only achieve moderate reductions, when implemented at all. In this work, we provide a more flexible framework where a neuron can be replaced with a linear combination of other neurons, improving the reduction. We apply this approach both on syntactic and semantic abstractions, and implement and evaluate them experimentally. Further, we introduce a refinement method for our abstractions, allowing for finding a better balance between reduction and precision.
{"title":"Syntactic vs Semantic Linear Abstraction and Refinement of Neural Networks","authors":"Calvin Chau, Jan Křetínský, S. Mohr","doi":"10.48550/arXiv.2307.10891","DOIUrl":"https://doi.org/10.48550/arXiv.2307.10891","url":null,"abstract":"Abstraction is a key verification technique to improve scalability. However, its use for neural networks is so far extremely limited. Previous approaches for abstracting classification networks replace several neurons with one of them that is similar enough. We can classify the similarity as defined either syntactically (using quantities on the connections between neurons) or semantically (on the activation values of neurons for various inputs). Unfortunately, the previous approaches only achieve moderate reductions, when implemented at all. In this work, we provide a more flexible framework where a neuron can be replaced with a linear combination of other neurons, improving the reduction. We apply this approach both on syntactic and semantic abstractions, and implement and evaluate them experimentally. Further, we introduce a refinement method for our abstractions, allowing for finding a better balance between reduction and precision.","PeriodicalId":335085,"journal":{"name":"Automated Technology for Verification and Analysis","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127583263","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-05DOI: 10.48550/arXiv.2306.02967
D. Egolf, S. Tripakis
Synthesis of distributed protocols is a hard, often undecidable, problem. Completion techniques provide partial remedy by turning the problem into a search problem. However, the space of candidate completions is still massive. In this paper, we propose optimization techniques to reduce the size of the search space by a factorial factor by exploiting symmetries (isomorphisms) in functionally equivalent solutions. We present both a theoretical analysis of this optimization as well as empirical results that demonstrate its effectiveness in synthesizing both the Alternating Bit Protocol and Two Phase Commit. Our experiments show that the optimized tool achieves a speedup of approximately 2 to 10 times compared to its unoptimized counterpart.
{"title":"Synthesis of Distributed Protocols by Enumeration Modulo Isomorphisms","authors":"D. Egolf, S. Tripakis","doi":"10.48550/arXiv.2306.02967","DOIUrl":"https://doi.org/10.48550/arXiv.2306.02967","url":null,"abstract":"Synthesis of distributed protocols is a hard, often undecidable, problem. Completion techniques provide partial remedy by turning the problem into a search problem. However, the space of candidate completions is still massive. In this paper, we propose optimization techniques to reduce the size of the search space by a factorial factor by exploiting symmetries (isomorphisms) in functionally equivalent solutions. We present both a theoretical analysis of this optimization as well as empirical results that demonstrate its effectiveness in synthesizing both the Alternating Bit Protocol and Two Phase Commit. Our experiments show that the optimized tool achieves a speedup of approximately 2 to 10 times compared to its unoptimized counterpart.","PeriodicalId":335085,"journal":{"name":"Automated Technology for Verification and Analysis","volume":"127 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123909876","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-25DOI: 10.48550/arXiv.2305.16212
Kenny Ballou, Elena Sherman
Value-based static analysis techniques express computed program invariants as logical formula over program variables. Researchers and practitioners use these invariants to aid in software engineering and verification tasks. When selecting abstract domains, practitioners weigh the cost of a domain against its expressiveness. However, an abstract domain's expressiveness tends to be stated in absolute terms; either mathematically via the sub-polyhedra the domain is capable of describing, empirically using a set of known properties to verify, or empirically via logical entailment using the entire invariant of the domain at each program point. Due to carry-over effects, however, the last technique can be problematic because it tends to provide a simplistic and imprecise comparisons. We address limitations of comparing, in general, abstract domains via logical entailment in this work. We provide a fixed-point algorithm for including the minimally necessary variables from each domain into the compared formula. Furthermore, we empirically evaluate our algorithm, comparing different techniques of widening over the Zones domain and comparing Zones to an incomparable Relational Predicates domain. Our empirical evaluation of our technique shows an improved granularity of comparison. It lowered the number of more precise invariants when comparing analysis techniques, thus, limiting the prevalent carry-over effects. Moreover, it removed undecidable invariants and lowered the number of incomparable invariants when comparing two incomparable relational abstract domains.
{"title":"Minimally Comparing Relational Abstract Domains","authors":"Kenny Ballou, Elena Sherman","doi":"10.48550/arXiv.2305.16212","DOIUrl":"https://doi.org/10.48550/arXiv.2305.16212","url":null,"abstract":"Value-based static analysis techniques express computed program invariants as logical formula over program variables. Researchers and practitioners use these invariants to aid in software engineering and verification tasks. When selecting abstract domains, practitioners weigh the cost of a domain against its expressiveness. However, an abstract domain's expressiveness tends to be stated in absolute terms; either mathematically via the sub-polyhedra the domain is capable of describing, empirically using a set of known properties to verify, or empirically via logical entailment using the entire invariant of the domain at each program point. Due to carry-over effects, however, the last technique can be problematic because it tends to provide a simplistic and imprecise comparisons. We address limitations of comparing, in general, abstract domains via logical entailment in this work. We provide a fixed-point algorithm for including the minimally necessary variables from each domain into the compared formula. Furthermore, we empirically evaluate our algorithm, comparing different techniques of widening over the Zones domain and comparing Zones to an incomparable Relational Predicates domain. Our empirical evaluation of our technique shows an improved granularity of comparison. It lowered the number of more precise invariants when comparing analysis techniques, thus, limiting the prevalent carry-over effects. Moreover, it removed undecidable invariants and lowered the number of incomparable invariants when comparing two incomparable relational abstract domains.","PeriodicalId":335085,"journal":{"name":"Automated Technology for Verification and Analysis","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126647075","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-17DOI: 10.48550/arXiv.2305.09901
Yushen Huang, Ertai Luo, Stanley Bak, Yifan Sun
Polynomial zonotopes, a non-convex set representation, have a wide range of applications from real-time motion planning and control in robotics, to reachability analysis of nonlinear systems and safety shielding in reinforcement learning. Despite this widespread use, a frequently overlooked difficulty associated with polynomial zonotopes is intersection checking. Determining whether the reachable set, represented as a polynomial zonotope, intersects an unsafe set is not straightforward. In fact, we show that this fundamental operation is NP-hard, even for a simple class of polynomial zonotopes. The standard method for intersection checking with polynomial zonotopes is a two-part algorithm that overapproximates a polynomial zonotope with a regular zonotope and then, if the overapproximation error is deemed too large, splits the set and recursively tries again. Beyond the possible need for a large number of splits, we identify two sources of concern related to this algorithm: (1) overapproximating a polynomial zonotope with a zonotope has unbounded error, and (2) after splitting a polynomial zonotope, the overapproximation error can actually increase. Taken together, this implies there may be a possibility that the algorithm does not always terminate.We perform a rigorous analysis of the method and detail necessary conditions for the union of overapproximations to provably converge to the original polynomial zonotope.
{"title":"On the Difficulty of Intersection Checking with Polynomial Zonotopes","authors":"Yushen Huang, Ertai Luo, Stanley Bak, Yifan Sun","doi":"10.48550/arXiv.2305.09901","DOIUrl":"https://doi.org/10.48550/arXiv.2305.09901","url":null,"abstract":"Polynomial zonotopes, a non-convex set representation, have a wide range of applications from real-time motion planning and control in robotics, to reachability analysis of nonlinear systems and safety shielding in reinforcement learning. Despite this widespread use, a frequently overlooked difficulty associated with polynomial zonotopes is intersection checking. Determining whether the reachable set, represented as a polynomial zonotope, intersects an unsafe set is not straightforward. In fact, we show that this fundamental operation is NP-hard, even for a simple class of polynomial zonotopes. The standard method for intersection checking with polynomial zonotopes is a two-part algorithm that overapproximates a polynomial zonotope with a regular zonotope and then, if the overapproximation error is deemed too large, splits the set and recursively tries again. Beyond the possible need for a large number of splits, we identify two sources of concern related to this algorithm: (1) overapproximating a polynomial zonotope with a zonotope has unbounded error, and (2) after splitting a polynomial zonotope, the overapproximation error can actually increase. Taken together, this implies there may be a possibility that the algorithm does not always terminate.We perform a rigorous analysis of the method and detail necessary conditions for the union of overapproximations to provably converge to the original polynomial zonotope.","PeriodicalId":335085,"journal":{"name":"Automated Technology for Verification and Analysis","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121161970","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-09DOI: 10.48550/arXiv.2305.05739
Kasper Engelen, Guillermo A. P'erez, Shrisha Rao
We study the complexity of reductions for weighted reachability in parametric Markov decision processes. That is, we say a state p is never worse than q if for all valuations of the polynomial indeterminates it is the case that the maximal expected weight that can be reached from p is greater than the same value from q. In terms of computational complexity, we establish that determining whether p is never worse than q is coETR-complete. On the positive side, we give a polynomial-time algorithm to compute the equivalence classes of the order we study for Markov chains. Additionally, we describe and implement two inference rules to under-approximate the never-worse relation and empirically show that it can be used as an efficient preprocessing step for the analysis of large Markov decision processes.
{"title":"Graph-Based Reductions for Parametric and Weighted MDPs","authors":"Kasper Engelen, Guillermo A. P'erez, Shrisha Rao","doi":"10.48550/arXiv.2305.05739","DOIUrl":"https://doi.org/10.48550/arXiv.2305.05739","url":null,"abstract":"We study the complexity of reductions for weighted reachability in parametric Markov decision processes. That is, we say a state p is never worse than q if for all valuations of the polynomial indeterminates it is the case that the maximal expected weight that can be reached from p is greater than the same value from q. In terms of computational complexity, we establish that determining whether p is never worse than q is coETR-complete. On the positive side, we give a polynomial-time algorithm to compute the equivalence classes of the order we study for Markov chains. Additionally, we describe and implement two inference rules to under-approximate the never-worse relation and empirically show that it can be used as an efficient preprocessing step for the analysis of large Markov decision processes.","PeriodicalId":335085,"journal":{"name":"Automated Technology for Verification and Analysis","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129318642","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-17DOI: 10.48550/arXiv.2304.08330
Ying Liu, Andrea Turrini, E. M. Hahn, Bai Xue, Lijun Zhang
In this paper, we propose an approximating framework for analyzing parametric Markov models. Instead of computing complex rational functions encoding the reachability probability and the reward values of the parametric model, we exploit the scenario approach to synthesize a relatively simple polynomial approximation. The approximation is probably approximately correct (PAC), meaning that with high confidence, the approximating function is close to the actual function with an allowable error. With the PAC approximations, one can check properties of the parametric Markov models. We show that the scenario approach can also be used to check PRCTL properties directly, without synthesizing the polynomial at first hand. We have implemented our algorithm in a prototype tool and conducted thorough experiments. The experimental results demonstrate that our tool is able to compute polynomials for more benchmarks than state of the art tools such as PRISM and Storm, confirming the efficacy of our PAC-based synthesis.
{"title":"Scenario Approach for Parametric Markov Models","authors":"Ying Liu, Andrea Turrini, E. M. Hahn, Bai Xue, Lijun Zhang","doi":"10.48550/arXiv.2304.08330","DOIUrl":"https://doi.org/10.48550/arXiv.2304.08330","url":null,"abstract":"In this paper, we propose an approximating framework for analyzing parametric Markov models. Instead of computing complex rational functions encoding the reachability probability and the reward values of the parametric model, we exploit the scenario approach to synthesize a relatively simple polynomial approximation. The approximation is probably approximately correct (PAC), meaning that with high confidence, the approximating function is close to the actual function with an allowable error. With the PAC approximations, one can check properties of the parametric Markov models. We show that the scenario approach can also be used to check PRCTL properties directly, without synthesizing the polynomial at first hand. We have implemented our algorithm in a prototype tool and conducted thorough experiments. The experimental results demonstrate that our tool is able to compute polynomials for more benchmarks than state of the art tools such as PRISM and Storm, confirming the efficacy of our PAC-based synthesis.","PeriodicalId":335085,"journal":{"name":"Automated Technology for Verification and Analysis","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127924633","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-10DOI: 10.48550/arXiv.2301.03915
Amit Gurung, Masaki Waga, Kohei Suenaga
Learning an automaton that approximates the behavior of a black-box system is a long-studied problem. Besides its theoretical significance, its application to search-based testing and model understanding is recently recognized. We present an algorithm to learn a nonlinear hybrid automaton (HA) that approximates a black-box hybrid system (HS) from a set of input--output traces generated by the HS. Our method is novel in handling (1) both exogenous and endogenous HS and (2) HA with reset associated with each transition. To our knowledge, ours is the first method that achieves both features. We applied our algorithm to various benchmarks and confirmed its effectiveness.
{"title":"Learning nonlinear hybrid automata from input-output time-series data","authors":"Amit Gurung, Masaki Waga, Kohei Suenaga","doi":"10.48550/arXiv.2301.03915","DOIUrl":"https://doi.org/10.48550/arXiv.2301.03915","url":null,"abstract":"Learning an automaton that approximates the behavior of a black-box system is a long-studied problem. Besides its theoretical significance, its application to search-based testing and model understanding is recently recognized. We present an algorithm to learn a nonlinear hybrid automaton (HA) that approximates a black-box hybrid system (HS) from a set of input--output traces generated by the HS. Our method is novel in handling (1) both exogenous and endogenous HS and (2) HA with reset associated with each transition. To our knowledge, ours is the first method that achieves both features. We applied our algorithm to various benchmarks and confirmed its effectiveness.","PeriodicalId":335085,"journal":{"name":"Automated Technology for Verification and Analysis","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128363284","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The C/C++11 (C11) standard offers a spectrum of ordering guarantees on memory access operations. The combinations of such orderings pose a challenge in developing correct and efficient weak memory programs. A common solution to preclude those program outcomes that violate the correctness specification is using C11 synchronization-fences, which establish ordering on program events. The challenge is in choosing a combination of fences that (i) restores the correctness of the input program, with (ii) as little impact on efficiency as possible (i.e., the smallest set of weakest fences). This problem is the optimal fence synthesis problem and is NP-hard for straight-line programs. In this work, we propose the first fence synthesis technique for C11 programs called FenSying and show its optimality. We additionally propose a near-optimal efficient alternative called fFenSying. We prove the optimality of FenSying and the soundness of fFenSying and present an implementation of both techniques. Finally, we contrast the performance of the two techniques and empirically demonstrate fFenSyings effectiveness.
C/ c++ 11 (C11)标准为内存访问操作提供了一系列排序保证。这些顺序的组合对开发正确有效的弱记忆程序提出了挑战。防止这些违反正确性规范的程序结果的常见解决方案是使用C11同步栅栏，它在程序事件上建立顺序。挑战在于选择一个栅栏的组合(i)恢复输入程序的正确性，(ii)对效率的影响尽可能小(即，最小的最弱栅栏集合)。该问题是最优围栏综合问题，是线性规划的np困难问题。本文提出了第一种用于C11程序的围栏合成技术——围栏合成，并证明了其最优性。我们还提出了一种近乎最优的高效替代方案，称为fFenSying。我们证明了分联的最优性和分联的合理性，并给出了两种技术的实现。最后，我们对比了两种技术的性能，并实证证明了fFenSyings的有效性。
{"title":"Fence Synthesis under the C11 Memory Model","authors":"Sanjana Singh, Divyanjali Sharma, Ishita Jaju, Subodh Sharma","doi":"10.48550/arXiv.2208.00285","DOIUrl":"https://doi.org/10.48550/arXiv.2208.00285","url":null,"abstract":"The C/C++11 (C11) standard offers a spectrum of ordering guarantees on memory access operations. The combinations of such orderings pose a challenge in developing correct and efficient weak memory programs. A common solution to preclude those program outcomes that violate the correctness specification is using C11 synchronization-fences, which establish ordering on program events. The challenge is in choosing a combination of fences that (i) restores the correctness of the input program, with (ii) as little impact on efficiency as possible (i.e., the smallest set of weakest fences). This problem is the optimal fence synthesis problem and is NP-hard for straight-line programs. In this work, we propose the first fence synthesis technique for C11 programs called FenSying and show its optimality. We additionally propose a near-optimal efficient alternative called fFenSying. We prove the optimality of FenSying and the soundness of fFenSying and present an implementation of both techniques. Finally, we contrast the performance of the two techniques and empirically demonstrate fFenSyings effectiveness.","PeriodicalId":335085,"journal":{"name":"Automated Technology for Verification and Analysis","volume":"398 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126756902","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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