Approximate planning and verification for large markov decision processes
Proceedings of the 27th Annual ACM Symposium on Applied Computing
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Approximate probabilistic model checking, and more generally sampling based model checking methods, proceed by drawing independent executions of a given model and by checking a temporal formula on these executions. In theory, these methods can be easily massively parallelized, but in practice one has to consider, for this purpose, important aspects such as the communication paradigm, the physical architecture of the machine, etc. Moreover, being able to develop multiple implementations of this algorithm on architectures as different as a cluster or many-cores requires various levels of expertise that may be problematic to gather. In this paper we propose to investigate the runtime behavior of approximate probabilistic model checking on various state of the art parallel machines - clusters, SMP, hybrid SMP clusters and the Cell processor - using a high-level parallel programming tool based on the Bulk Synchronous Parallelism paradigm to quickly instantiate model checking problems over a large variety of parallel architectures. Our conclusion assesses the relative efficiency of these architectures with respect to the algorithm classes and promotes guidelines for further work on parallel APMC implementation.