ParFuse: Parallel and Compositional Analysis of Message Passing Programs

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• 刊名：Lecture Notes in Computer Science
• 出版年：2017
• 出版时间：2017
• 年：2017
• 卷：10136
• 期：1
• 页码：24-39
• 丛书名：Languages and Compilers for Parallel Computing
• ISBN：978-3-319-52709-3
• 卷排序：10136

文摘

Static analysis discovers provable true properties about behaviors of programs that are useful in optimization, debugging and verification. Sequential static analysis techniques fail to interpret the message passing semantics of the MPI and lack the ability to optimize or check the message passing behaviors of MPI programs. In this paper, we introduce an abstraction for approximating the message passing behaviors of MPI programs that is more precise than prior work and is applicable to a wide variety of applications. Our approach builds on the compositional paradigm where we transparently extend MPI support to sequential analyses through composition with our MPI analyses. This is the first framework where the data flow analysis is carried out in parallel on a cluster, with the message-carried data flow facts for refining inter-process data flow analysis states. We detail ParFuse – a framework that supports such parallel and compositional analysis of MPI programs, report its scalability and detail the prospects of extending our work for more powerful analyses.