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Issue No.12 - Dec. (2012 vol.18)
pp: 2467-2476
The performance of massively parallel applications is often heavily impacted by the cost of communication among compute nodes. However, determining how to best use the network is a formidable task, made challenging by the ever increasing size and complexity of modern supercomputers. This paper applies visualization techniques to aid parallel application developers in understanding the network activity by enabling a detailed exploration of the flow of packets through the hardware interconnect. In order to visualize this large and complex data, we employ two linked views of the hardware network. The first is a 2D view, that represents the network structure as one of several simplified planar projections. This view is designed to allow a user to easily identify trends and patterns in the network traffic. The second is a 3D view that augments the 2D view by preserving the physical network topology and providing a context that is familiar to the application developers. Using the massively parallel multi-physics code pF3D as a case study, we demonstrate that our tool provides valuable insight that we use to explain and optimize pF3D's performance on an IBM Blue Gene/P system.
Hardware, Data visualization, Layout, Performance evaluation, Supercomputers, Network topology, Computational modeling, projected graph layouts, Performance analysis, network traffic visualization
A. G. Landge, J. A. Levine, A. Bhatele, K. E. Isaacs, T. Gamblin, M. Schulz, S. H. Langer, Peer-Timo Bremer, V. Pascucci, "Visualizing Network Traffic to Understand the Performance of Massively Parallel Simulations", IEEE Transactions on Visualization & Computer Graphics, vol.18, no. 12, pp. 2467-2476, Dec. 2012, doi:10.1109/TVCG.2012.286
[1] TOP500 Supercomputing Sites., Nov. 2011.
[2] D. Archambault, T. Munzner, and D. Auber, Topolayout: Multilevel graph layout by topological features IEEE Trans. Vis. Comput. Graph., 13(2): 305-317, 2007.
[3] R. A Becker,S. G. Eick,, and A. R., Wilks. Visualizing network data IEEE Trans. Vis. Comput. Graph., 1(1): 16-28, 1995.
[4] R. Bell,A. D. Malony,, and S. Shende., ParaProf: A portable, extensible, and scalable tool for parallel performance profile analysis. In Euro-Par 2003. Parallel Processing, pages 17-26, 2003.
[5] R. L. Berger,B. F. Lasinski,A. B. Langdon,T. B. Kaiser,B. B. Afeyan,B. I. Cohen,C. H. Still,, and E. A. Williams., Influence of spatial and temporal laser beam smoothing on stimulated Brillouin scattering in fila-mentary laser light. Phvs. Rev. Lett., 75: 1078-1081, Aug 1995.
[6] N. Bhatia, F. Song, F. Wolf, J. Dongarra, B. Mohr,, and S. Moore., Automatic experimental analysis of communication patterns in virtual topolo-gies. In International Conf. on Parallel Processing, pages 465-472, 2005.
[7] U. Brandes and C. Pich., An experimental study on distance-based graph drawing. In Inti. Symp. on Graph Drawing, pages 218-229, 2008.
[8] A. Chan, W. Gropp, and E. L. Lusk., An efficient format for nearly constant-time access to arbitrary time intervals in large trace files. Scientific Programming; 16(2-3): 155-165, 2008.
[9] I.-H. Chung, R. Walkup, H.-F. Wen,, and H. Yu., MPI tools and performance studies - MPI performance analysis tools on Blue Gene/L. In Proceedings of the ACM/EEE SC2006 Conference on High Performance Networking and Computing, page 123, 2006.
[10] C. C. Evans., The official YAML web site., Sept. 2011.
[11] E. R. Gansner, Y. Hu, S. C. North,, and C. E., Scheidegger. Multilevel agglomerative edge bundling for visualizing large graphs. In IEEE Pacific Visualization Symposium, pages 187-194, 2011.
[12] E. R Gansner, Y. Koren, and S. C. North., Topological fisheye views for visualizing large graphs IEEE Trans. Vis. Comput. Graph., 11(4): 457-468, 2005.
[13] M. Geimer, F. Wolf, B. J. N. Wylie, E. Abraham, D. Becker,, and B. Mohr., The Scalasca performance toolset architecture. Concurrency and Computation: Practice and Experience, 22(6): 702-719, 2010.
[14] M. T Heath and J. A. Etheridge., Visualizing the performance of parallel programs IEEE Software, 8(5): 29-39, 1991.
[15] I. Herman,G. Melançon,, and M. S., Marshall. Graph visualization and navigation in information visualization: A survey. IEEE Trans. Vis. Comput. Graph., 6(1): 24-43, 2000.
[16] L. Kalé and S. Krishnan., CHARM++: A Portable Concurrent Object Oriented System Based on C++. In A. Paepcke, editor, Proceedings of OOPSLA’93, pages 91-108. ACM Press, September 1993.
[17] A. Knüpfer, H. Brunst, J. Doleschal, M. Jurenz, M. Lieber, H. Mickler, M. S. Muller,, and W. E. Nagel., The Vampir performance analysis tool-set. In Parallel Tools Workshop, pages 139-155, 2008.
[18] J. Liang and M. L. Huang., Highlighting in information visualization: A survey. In Information Visualisation IV, pages 79-85, July 2010.
[19] F. Mansmann, F. Fischer, D. A. Keim,, and S. C. North., Visual support for analyzing network traffic and intrusion detection events using TreeMap and graph representations. In 3rd ACM Symposium on Computer Human Interaction for Management of Information Technology, 2009.
[20] J. McPherson, K.-L. Ma, P. Krystosk, T. Bartoletti, and M. Chris-tensen., PortVis: a tool for port-based detection of security events. In VizSEC/DMSEC: ACM workshop on Visualization and Data Mining for Computer Security, pages 73-81, 2004.
[21] T. Moscovich, F. Chevalier, N. Henry, E. Pietriga, and J.-D. Fekete., Topology-aware navigation in large networks. In 27th International Conf. on Human Factors in Computing Systems, pages 2319-2328, 2009.
[22] E. I Moses., Overview of the national ignition facility Fusion Science and Technology, 54(2): 361-366, 2008.
[23] C. Muelder, F. Gygi, and K.-L. Ma, Visual analysis of inter-process communication for large-scale parallel computing IEEE Trans. Vis. Comput. Graph., 15(6): 1129-1136, 2009.
[24] G. Namata, B. Staats, L. Getoor,, and B. Shneiderman., A dual-view approach to interactive network visualization. In Conference on Information and Knowledge Management, pages 939-942, 2007.
[25] T. E Oliphant, Guide to NumPy. Provo. UT. Mar. 2006.
[26] A. J. Quigley and P. Eades., Fade: Graph drawing, clustering, and visual abstraction. In Intl. Symp. on Graph Drawing, pages 197-210, 2000.
[27] P. G. Raponi, F. Petrini, R. Walkup,, and F. Checconi., Characterization of the communication patterns of scientific applications on Blue Gene/P. In 25th IEEE International Symposium on Parallel and Distributed Processing, pages 1017-1024, 2011.
[28] N. H Riche, B. Lee, and C. Plaisant., Understanding interactive legends: a comparative evaluation with standard widgets Comput. Graph. Forum, 29(3): 1193-1202, 2010.
[29] L. M Schnorr, G. Huard, and P. O. A. Navaux., Triva: Interactive 3D visualization for performance analysis of parallel applications Future Generation Compo Syst., 26(3): 348-358, 2010.
[30] M. Schulz and B. R de Supinski, PNMPI Tools: a Whole Lot Greater than the Sum of their Parts. In Proceedings of SC07, 2007.
[31] M. Schulz,J. A. Levine, P.-T. Bremer, T. Gamblin,, and V. Pascucci., In-terpreting performance data across intuitive domains. In International Conference on Parallel Processing, pages 206-215, 2011.
[32] S. Shende and A. D Malony., The Tau parallel performance system Int. J. of High Perf Compo Appl., 20(2): 287-311, 2006.
[33] B. Shneiderman., The eyes have it: a task by data type taxonomy for information visualizations. In Visual Languages, 1996. Proceedings., IEEE Symposium on, pages 336-343, sep 1996.
[34] B. E. Smith and B. Bode., Performance effects of node mappings on the IBM BlueGene/L machine. In Euro-Par. pages 1005-1013, 2005.
[35] C. H Still,R. L. Berger,A. B. Langdon,D. E. Hinkel,L. J. Suter,, and E. A. Williams., Filamentation and forward Brillouin scatter of entire smoothed and aberrated laser beams Physics of Plasmas, 7(5): 2023-2032, 2000.
[36] K. L Summers,T. P. Caudell, K. Berkbigler, B. Bush, K. Davis, and S. Smith., Graph visualization for the analysis of the structure and dynamics of extreme-scale supercomputers Information Visualization, 3(3): 209-222, Sept. 2004.
[37] G. van Rossum., Python tutorial. Technical Report CS-R9526, Centrum voor Wiskunde en Informatica (CWI), 1995.
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