Combining in-situ and in-transit processing to enable extreme-scale scientific analysis
SC '12 Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis
Versatile communication algorithms for data analysis
EuroMPI'12 Proceedings of the 19th European conference on Recent Advances in the Message Passing Interface
Proceedings of the 18th ACM SIGPLAN symposium on Principles and practice of parallel programming
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Topology-based techniques are useful for multiscale exploration of the feature space of scalar-valued functions, such as those derived from the output of large-scale simulations. The Morse-Smale (MS) complex, in particular, allows robust identification of gradient-based features, and therefore is suitable for analysis tasks in a wide range of application domains. In this paper, we develop a two-stage algorithm to construct the 1-skeleton of the Morse-Smale complex in parallel, the first stage independently computing local features per block and the second stage merging to resolve global features. Our implementation is based on MPI and a distributed-memory architecture. Through a set of scalability studies on the IBM Blue Gene/P supercomputer, we characterize the performance of the algorithm as block sizes, process counts, merging strategy, and levels of topological simplification are varied, for datasets that vary in feature composition and size. We conclude with a strong scaling study using scientific datasets computed by combustion and hydrodynamics simulations.