The Community for Technology Leaders
RSS Icon
Issue No.12 - Dec. (2011 vol.17)
pp: 1785-1794
Teng-Yok Lee , The Ohio State University
Han-Wei Shen , The Ohio State University
Because of the ever increasing size of output data from scientific simulations, supercomputers are increasingly relied upon to generate visualizations. One use of supercomputers is to generate field lines from large scale flow fields. When generating field lines in parallel, the vector field is generally decomposed into blocks, which are then assigned to processors. Since various regions of the vector field can have different flow complexity, processors will require varying amounts of computation time to trace their particles, causing load imbalance, and thus limiting the performance speedup. To achieve load-balanced streamline generation, we propose a workload-aware partitioning algorithm to decompose the vector field into partitions with near equal workloads. Since actual workloads are unknown beforehand, we propose a workload estimation algorithm to predict the workload in the local vector field. A graph-based representation of the vector field is employed to generate these estimates. Once the workloads have been estimated, our partitioning algorithm is hierarchically applied to distribute the workload to all partitions. We examine the performance of our workload estimation and workload-aware partitioning algorithm in several timings studies, which demonstrates that by employing these methods, better scalability can be achieved with little overhead.
Flow visualization, Parallel processing, 3D vector field visualization, Streamlines.
Teng-Yok Lee, Han-Wei Shen, "Load-Balanced Parallel Streamline Generation on Large Scale Vector Fields", IEEE Transactions on Visualization & Computer Graphics, vol.17, no. 12, pp. 1785-1794, Dec. 2011, doi:10.1109/TVCG.2011.219
[1] D. P. Bertsekas, Nonlinear Programming. Athena Scientific, 2nd edition, 1999.
[2] D. Camp, C. Garth, H. Childs, D. Pugmire, and K. Joy, Streamline integration using MPI-hybrid parallelism on large Multi-Core architecture. IEEE Transactions on Visualization and Computer Graphics, 99(PrePrints), 2010.
[3] J. H. Chen, A. Choudhary, B. de Supinski, M. DeVries, E. R. Hawkes, S. Klasky, W. K. Liao, K. L. Ma, J. Mellor-Crummey, N. Podhorszki, R. Sankaran, S. Shende, and C. S. Yoo, Terascale direct numerical simulations of turbulent combustion using s3d. Computational Science & Discovery, 2:015001, 2009.
[4] L. Chen and I. Fujishiro, Optimizing parallel performance of streamline visualization for large distributed flow datasets. In PacficVis '08: Proceedings of the IEEE Pacific Visualization Symposium 2008, pages 87–94, Mar. 2008.
[5] J. Clyne, P. Mininni, A. Norton, and M. Rast, Interactive desktop analysis of high resolution simulations: application to turbulent plume dynamics and current sheet formation. New Journal of Physics, 9, 2007.
[6] J. Dean and S. Ghemawat, Mapreduce: simplified data processing on large clusters. Communications of the ACM, 51 (1): 107–113, Jan. 2008.
[7] K. D. Devine, E. G. Boman, R. T. Heaphy, R. H. Bisseling, and U. V. Catalyurek. Parallel hypergraph partitioning for scientific computing. In IPDPS '06: Proceedings of the International Parallel and Distributed Processing Symposium 2006, pages 10 pp.+, 2006.
[8] T. F. Fric and A. Roshko, Vortical structure in the wake of a transverse jet. Journal of Fluid Mechanics, 279: 1–47, 2005.
[9] H. G. Lagrangian, structures and the rate of strain in a partition of twodimensional turbulence. Physics of Fluids, 13 (11): 3365–3385, 2005.
[10] M. R. Garey, S. D. Johnson, and L. Stockmeyer, Some simplified NP-complete problems. In STOC '74: Proceedings of the ACM Symposium on Theory of computing 1974, pages 47–63, 2005.
[11] W. Gropp, E. Lusk, and A. Skjellum, Using MPI: Portable Parallel Programming with the Message Passing Interface, 2nd edition. MIT Press, Cambridge, MA, 1999.
[12] R. W. Grout, A. Gruber, C. Yoo, and J. Chen, Direct numerical simulation of flame stabilization downstream of a transverse fuel jet in cross-flow. In Proceedings of the Combustion Institute, volume 33, pages 1629–1637, 2005.
[13] W. W. Hager and Y. Krylyuk, Graph partitioning and continuous quadratic programming. SIAM Journal on Discrete Mathematics, 12: 500–523, Oct. 1999.
[14] G. Karypis and V. Kumar, A fast and high quality multilevel scheme for partitioning irregular graphs. SIAM J. Sci. Comput., 20 (1): 359–392, Dec. 1998.
[15] W. Kendall, J. Wang, M. Allen, T. Peterka, J. Huang, and D. Erickson, Simplified parallel domain traversal. In SC '11: Proceedings of the ACM/IEEE Conference on Supercomputing 2011, 2011. To appear.
[16] M. E. Maltrud and J. L. McClean, An eddy resolving global 1/10 ocean simulation. Ocean Modelling, 8(1–2): 31, 2005.
[17] E. Merzari, W. Pointer, A. Obabko, and P. Fischer, On the numerical simulation of thermal striping in the upper plenum of a fast reactor. In ICAPP '10: Proceedings of the International Congress on Advances in Nuclear Power Plants 2010, 2010.
[18] J. C. Meza, R. A. Oliva, P. D. Hough, and P. J. Williams, Opt++: An object-oriented toolkit for nonlinear optimization. ACM Transactions on Mathematical Software, 33 (2), June 2007.
[19] J. Nocedal, Updating Quasi-Newton matrices with limited storage. Mathematics of Computation, 35 (151): 773–782, 2005.
[20] T. Peterka, R. Ross, B. Nouanesengsey, T.-Y. Lee, H.-W. Shen, W. Kendall, and J. Huang, A study of parallel particle tracing for steady-state and time-varying flow fields. In IPDPS '11: Proceedings of IEEE International Parallel & Distributed Processing Symposium 2011, 2011. To appear.
[21] D. Pugmire, H. Childs, C. Garth, S. Ahern, and G. H. Weber, Scalable computation of streamlines on very large datasets. In SC '09: Proceedings of the ACM/IEEE Conference on Supercomputing 2009, pages 16:1– 16:12, 2009.
[22] K. Schloegel, G. Karypis, and V. Kumar, Graph partitioning for high-performance scientific simulations. In J. Dongarra, I. Foster, G. Fox, W. Gropp, K. Kennedy, L. Torczon, and A. White editors, Sourcebook of parallel computing, chapter 18, pages 491–541. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, Nov 2002.
[23] L. Xu, and H.-W. Shen, Flow web: a graph based user interface for 3D flow field exploration. In Proceedings of the IS&T/SPIE Visualization and Data 2010, Jan. 2010.
[24] H. Yu, C. Wang, and K. L. Ma, Parallel hierarchical visualization of large time-varying 3D vector fields. In SC 07: Proceedings of the ACM/IEEE Conference on Supercomputing 2007', 2007.
17 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool