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Issue No.11 - November (2011 vol.17)
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.
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 & Computer Graphics, vol.17, no. 11, pp. 1702-1713, November 2011, doi:10.1109/TVCG.2010.259
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