Abstract
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.
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This research was supported in part by NSF ACI 1148127, CCF 1439002, CCF 1346756 and DOE grant “Static Analysis using ROSE”.
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Aananthakrishnan, S., Bronevetsky, G., Baranowski, M., Gopalakrishnan, G. (2017). ParFuse: Parallel and Compositional Analysis of Message Passing Programs. In: Ding, C., Criswell, J., Wu, P. (eds) Languages and Compilers for Parallel Computing. LCPC 2016. Lecture Notes in Computer Science(), vol 10136. Springer, Cham. https://doi.org/10.1007/978-3-319-52709-3_3
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DOI: https://doi.org/10.1007/978-3-319-52709-3_3
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