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Issue No. 11 - November (2011 vol. 17)
ISSN: 1077-2626
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.
Concurrent programming, parallel programming, modes of computation, parallelism and concurrency, picture/image generation, display algorithms.

K. I. Joy, D. Camp, C. Garth, H. Childs and D. Pugmire, "Streamline Integration Using MPI-Hybrid Parallelism on a Large Multicore Architecture," in IEEE Transactions on Visualization & Computer Graphics, vol. 17, no. , pp. 1702-1713, 2010.
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