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Streamline Integration Using MPI-Hybrid Parallelism on a Large Multicore Architecture
November 2011 (vol. 17 no. 11)
pp. 1702-1713
David Camp, Lawrence Berkeley National Laboratory, Berkeley and University of California, Davis, Davis
Christoph Garth, University of California, Davis, Davis
Hank Childs, Lawrence Berkeley National Laboratory, Berkeley and University of California, Davis, Davis
Dave Pugmire, Oak Ridge National Laboratory, Oak Ridge
Kenneth I. Joy, University of California, Davis, Davis
Streamline computation in a very large vector field data set represents a significant challenge due to the nonlocal and data-dependent nature of streamline integration. In this paper, we conduct a study of the performance characteristics of hybrid parallel programming and execution as applied to streamline integration on a large, multicore platform. With multicore processors now prevalent in clusters and supercomputers, there is a need to understand the impact of these hybrid systems in order to make the best implementation choice. We use two MPI-based distribution approaches based on established parallelization paradigms, parallelize over seeds and parallelize over blocks, and present a novel MPI-hybrid algorithm for each approach to compute streamlines. Our findings indicate that the work sharing between cores in the proposed MPI-hybrid parallel implementation results in much improved performance and consumes less communication and I/O bandwidth than a traditional, nonhybrid distributed implementation.

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Index Terms:
Concurrent programming, parallel programming, modes of computation, parallelism and concurrency, picture/image generation, display algorithms.
Citation:
David Camp, Christoph Garth, Hank Childs, Dave Pugmire, Kenneth I. Joy, "Streamline Integration Using MPI-Hybrid Parallelism on a Large Multicore Architecture," IEEE Transactions on Visualization and Computer Graphics, vol. 17, no. 11, pp. 1702-1713, Nov. 2011, doi:10.1109/TVCG.2010.259
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