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Large-Scale Data Visualization Using Parallel Data Streaming
July/August 2001 (vol. 21 no. 4)
pp. 34-41
Effective large-scale data visualization remains an important challenge with analysis codes already producing terabyte results on clusters with thousands of processors. Frequently the analysis codes produce distributed data and consume a significant portion of the available memory per node. This article presents an architectural approach to handling these visualization problems based on parallel data streaming to enable visualizations on a parallel cluster. The authors' approach requires less memory than other visualizations while achieving high code reuse.

1. W. Schroeder, K. Martin, and B. Lorensen, The Visualization Toolkit: An Object-Oriented Approach to 3D Graphics, second ed. Upper Saddle River, N.J.: Prentice Hall, 1998 (the visualization toolkit, at).
2. S.G. Parker, D.M. Weinstein, and C.R. Johnson, "The SCIRun Computational Steering Software System," Modern Software Tools in Scientific Computing, Birkhauser Press, Boston, 1997, pp. 1-44.
3. S. Molnar et al., "A Sorting Classification of Parallel Rendering," IEEE Computer Graphics and Applications, vol. 14, no. 4, pp. 23-32, July 1994.
4. M.B. Cox and D. Ellsworth, "Application-Controlled Demand Paging for Out-of-Core Visualization," Proc. Visualization 97, ACM Press, New York, Oct. 1997, pp. 235-244.
5. M. Cox and D. Ellsworth, "Managing Big Data for Scientific Visualization," Exploring Gigabyte Datasets in Real-Time: Algorithms, Data Management, and Time-Critical Design, Siggraph 97, Course Notes 4, ACM Press, New York, 1997.
6. Y. Chiang, C.T. Silva, and W.J. Schroeder, “Interactive Out-of-Core Isosurface Extraction,” Proc. Visualization 1998, pp. 167-174, Oct. 1998.
7. T.A. Funkhouser et al., "Database Management for Models Larger Than Main Memory," Interactive Walkthrough of Large Geometric Databases, Course Notes 32, Siggraph 95, ACM Press, New York, 1995.
8. T. Itoh and K. Koyamada, “Automatic Isosurface Propagation by Using an Extrema Graph and Sorted Boundary Cell Lists,” IEEE Trans. Visualization and Computer Graphics, vol. 1, no. 4, pp. 319-327, Dec. 1995.
9. S. Subramanian and S. Ramaswamy, "The P-Range Tree: A New Data Structure for Range Searching in Secondary Memory," Proc. ACM/SIAM Symp. Discrete Algorithms, SIAM,Philadelphia, Pa., 1995, pp. 378-387.
10. S. Teller et al., "Partitioning and Ordering Large Radiosity Computations," Proc. Siggraph 94, ACM Press, New York, 1994, pp. 443-450.
11. S.-K. Ueng, K. Sikorski, and K.-L. Ma, "Out-of-Core Streamline Visualization on Large Unstructured Meshes," IEEE Trans. Visualization and Computer Graphics, vol. 3, no. 4, Dec. 1997, pp. 370-380.
12. D. Song and E. Golin, "Fine-Grain Visualization Algorithms in Dataflow Environments," Proc. IEEE Visualization 1993, IEEE CS Press, Los Alamitos, Calif., 1993, pp. 126-133.
13. G. Abram and L. Treinish, "An Extended Data-Flow Architecture for Data Analysis and Visualization," IEEE Visualization 95 Conf. Proc., IEEE Computer Society Press, Los Alamitos, Calif., Oct. 1995, pp. 263-270.
14. C. Upson et al., "The Application Visualization System: A Computational Environment for Scientific Visualization," IEEE Computer Graphics and Applications, Vol. 9, No. 4, July 1989, pp. 30-42.
15. M. Krogh and C. Hansen, "Visualization on Massively Parallel Computers using CM/AVS," AVS Users Conf., 1993, pp. 129-137, http://www.ticam.utexas.edu/CCV/papers/papera1.pdfhttp:/ /www.kitware.com/vtk.htmlhttp:/ /www.kitware.com/vtk.htmlhttp:/ /www.acl.lanl.gov/Viz/abstractsParallelAC-AVS.html .
16. C.R. Johnson and S. Parker, "The SCIRun Parallel Scientific Computing Problem-Solving Environment," Ninth SIAM Conf. Parallel Processing for Scientific Computing, SIAM, Philadelphia, Pa., 1999.
17. M. Miller, C. Hansen, and C. Johnson, "Simulation Steering with SCIRun in a Distributed Environment," Applied Parallel Computing, Fourth Int'l Workshop(PARA 98), Lecture Notes in Computer Science, no. 1541, B. Kagström, J. Dongarra, E. Elmroth, and J. Wasniewski, eds., Springer-Verlag, Berlin, 1998, pp. 366-376.
18. P.J. Moran and C. Henze, “Large Field Visualization with Demand-Driven Calculation,” Proc. IEEE Visualization 1999, D. Ebert, M. Gross, and B. Hamann, eds., pp. 27-34, Oct. 1999.
19. R. Haimes and D.E. Edwards, Visualization in a Parallel Processing Environment, American Inst. of Aeronautics and Astronautics, Reston, Va., 1997.
20. C.C. Law et al., "A Multithreaded Streaming Pipeline Architecture for Large Structured Data Sets," Proc. IEEE Visualization 1999, ACM Press, New York, 1999, pp. 225-232.
21. W. Gropp, E. Lusk, and A. Skjellum, Using MPI: Portable Parallel Programming with the Message Passing Interface. MIT Press, 1994.
22. G. Humphreys et al., "Distributed Rendering for Scalable Displays," Proc. IEEE/ACM Supercomputing Conf.(SC 2000), CD-ROM, ACM Press, New York, Nov. 2000.

Citation:
James Ahrens, Kristi Brislawn, Ken Martin, Berk Geveci, C. Charles Law, Michael Papka, "Large-Scale Data Visualization Using Parallel Data Streaming," IEEE Computer Graphics and Applications, vol. 21, no. 4, pp. 34-41, July-Aug. 2001, doi:10.1109/38.933522
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