Adaptively parallel runtime verification based on distributed network for temporal properties

IF 2 4区 计算机科学 Q2 COMPUTER SCIENCE, THEORY & METHODS Parallel Computing Pub Date : 2023-09-01 DOI:10.1016/j.parco.2023.103034
Bin Yu , Xu Lu , Cong Tian , Meng Wang , Chu Chen , Ming Lei , Zhenhua Duan
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Abstract

Runtime verification is a lightweight verification technique that verifies whether a monitored program execution satisfies a desired property. Online runtime verification faces challenges regarding efficiency and property expressiveness, which limit its widespread adoption. However, there is a lack of research that addresses both of these issues. With the basis of a distributed network, we propose an adaptively parallel approach to verify full regular temporal properties of C programs in an online manner. During program execution, segments of the generated state sequence are verified by distributed machines concurrently, while each segment is also verified in each multi-core machine with an adaptive number of threads. Experimental results demonstrate that, with supporting more expressive properties, our approach has a speedup of 2.5X–5.0X compared with other runtime verification approaches.

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基于分布式网络的时间属性自适应并行运行时验证
运行时验证是一种轻量级的验证技术,用于验证被监视的程序执行是否满足所需的属性。在线运行时验证面临着效率和属性表达性方面的挑战,这限制了它的广泛采用。然而,缺乏解决这两个问题的研究。在分布式网络的基础上,我们提出了一种自适应并行方法来在线验证C程序的全正则时间特性。在程序执行过程中,生成的状态序列的段由分布式机器并发地进行验证,同时每个段也在每个多核机器中以自适应的线程数进行验证。实验结果表明,与其他运行时验证方法相比,我们的方法在支持更多表达属性的情况下,速度提高了2.5 - 5.0 x。
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来源期刊
Parallel Computing
Parallel Computing 工程技术-计算机:理论方法
CiteScore
3.50
自引率
7.10%
发文量
49
审稿时长
4.5 months
期刊介绍: Parallel Computing is an international journal presenting the practical use of parallel computer systems, including high performance architecture, system software, programming systems and tools, and applications. Within this context the journal covers all aspects of high-end parallel computing from single homogeneous or heterogenous computing nodes to large-scale multi-node systems. Parallel Computing features original research work and review articles as well as novel or illustrative accounts of application experience with (and techniques for) the use of parallel computers. We also welcome studies reproducing prior publications that either confirm or disprove prior published results. Particular technical areas of interest include, but are not limited to: -System software for parallel computer systems including programming languages (new languages as well as compilation techniques), operating systems (including middleware), and resource management (scheduling and load-balancing). -Enabling software including debuggers, performance tools, and system and numeric libraries. -General hardware (architecture) concepts, new technologies enabling the realization of such new concepts, and details of commercially available systems -Software engineering and productivity as it relates to parallel computing -Applications (including scientific computing, deep learning, machine learning) or tool case studies demonstrating novel ways to achieve parallelism -Performance measurement results on state-of-the-art systems -Approaches to effectively utilize large-scale parallel computing including new algorithms or algorithm analysis with demonstrated relevance to real applications using existing or next generation parallel computer architectures. -Parallel I/O systems both hardware and software -Networking technology for support of high-speed computing demonstrating the impact of high-speed computation on parallel applications
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