A Step towards Making Local and Remote Desktop Applications Interoperable with High-Resolution Tiled Display Walls

  • Tor-Magne Stien Hagen
  • Daniel Stødle
  • John Markus Bjørndalen
  • Otto Anshus
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6723)

Abstract

The visual output from a personal desktop application is limited to the resolution of the local desktop and display. This prevents the desktop application from utilizing the resolution provided by high-resolution tiled display walls. Additionally, most desktop applications are not designed for the distributed and parallel architecture of display walls, limiting the availability of such applications in these kinds of environments. This paper proposes the Network Accessible Compute (NAC) model, transforming personal computers into compute services for a set of display-side visualization clients. The clients request output from the compute services, which in turn start the relevant personal desktop applications and use them to produce output that can be transferred into display-side compatible formats by the NAC service. NAC services are available to the visualization clients through a live data set, which receives requests from visualization nodes, translates these to compute messages and forwards them to available compute services. Compute services return output to visualization nodes for rendering. Experiments conducted on a 28-node, 22-megapixel, display wall show that the time used to rasterize a 350-page PDF document into 550 megapixels of image tiles and display these image tiles on the display wall is 74.7 seconds (PNG) and 20.7 seconds (JPG) using a single computer with a quad-core CPU as a NAC service. When increasing this into 28 quad-core CPU computers, this time is reduced to 4.2 seconds (PNG) and 2.4 seconds (JPG). This shows that the application output from personal desktop computers can be made interoperable with high-resolution tiled display walls, with good performance and independent of the resolution of the local desktop and display.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Abdennadher, N., Boesch, R.: Towards a peer-to-peer platform for high performance computing. In: HPCASIA 2005: Proceedings of the Eighth International Conference on High-Performance Computing in Asia-Pacific Region, p. 354. IEEE Computer Society, Washington, DC, USA (2005)Google Scholar
  2. 2.
    Anderson, D.P.: Boinc: A system for public-resource computing and storage. In: 5th IEEE/ACM International Workshop on Grid Computing, pp. 4–10 (2004)Google Scholar
  3. 3.
    Anderson, D.P., Cobb, J., Korpela, E., Lebofsky, M., Werthimer, D.: Seti@home: an experiment in public-resource computing. Commun. ACM 45(11), 56–61 (2002)CrossRefGoogle Scholar
  4. 4.
    Andrade, H., Kurc, T., Sussman, A., Saltz, J.: Active semantic caching to optimize multidimensional data analysis in parallel and distributed environments. Parallel Comput. 33(7-8), 497–520 (2007)CrossRefGoogle Scholar
  5. 5.
    Beynon, M.D., Kurc, T., Çatalyürek, U., Chang, C., Sussman, A., Saltz, J.: Distributed processing of very large datasets with datacutter. Clusters and Computational Grids for Scientific Computing 27(11), 1457–1478 (2001)MATHGoogle Scholar
  6. 6.
    Cecile, G.F., Fedak, G., Germain, C., Neri, V.: Xtremweb: A generic global computing system. In: Proceedings of the IEEE International Symposium on Cluster Computing and the Grid (CCGRID 2001), pp. 582–587 (2001)Google Scholar
  7. 7.
    Correa, W.T., Klosowski, J.T., Morris, C.J., Jackmann, T.M.: SPVN: a new application framework for interactive visualization of large datasets. In: SIGGRAPH 2007: ACM SIGGRAPH 2007 courses, page 6 (2007)Google Scholar
  8. 8.
    Hagen, T.-M.S., Stødle, D., Anshus, O.: On-demand high-performance visualization of spatial data on high-resolution tiled display walls. In: Proceedings of the International Conference on Information Visualization Theory and Applications, pp. 112–119 (2010)Google Scholar
  9. 9.
    Jeong, B., Renambot, L., Jagodic, R., Singh, R., Aguilera, J., Johnson, A., Leigh, J.: High-performance dynamic graphics streaming for scalable adaptive graphics environment. In: SC 2006: Proceedings of the 2006 ACM/IEEE Conference on Supercomputing, page 108 (2006)Google Scholar
  10. 10.
    Katz, D., Bergou, A., Berriman, G., Block, G., Collier, J., Curkendall, D., Good, J., Husman, L., Jacob, J., Laity, A., Li, P., Miller, C., Prince, T., Siegel, H., Williams, R.: Accessing and visualizing scientific spatiotemporal data. In: Proceedings of 16th International Conference on Scientific and Statistical Database Management, 2004, pp. 107–110 (June 2004)Google Scholar
  11. 11.
    Kurc, T., Çatalyürek, U., Chang, C., Sussman, A., Saltz, J.: Visualization of large data sets with the active data repository. IEEE Comput. Graph. Appl. 21(4), 24–33 (2001)CrossRefGoogle Scholar
  12. 12.
    Li, P.: Supercomputing visualization for earth science datasets. In: Proceedings of 2002 NASA Earth Science Technology Conference (2002)Google Scholar
  13. 13.
    Li, P., Duquette, W.H., Curkendall, D.W.: Riva: A versatile parallel rendering system for interactive scientific visualization. IEEE Transactions on Visualization and Computer Graphics 2(3), 186–201 (1996)CrossRefGoogle Scholar
  14. 14.
    Litzkow, M., Livny, M., Mutka, M.: Condor - a hunter of idle workstations. In: Proceedings of the 8th International Conference of Distributed Computing Systems (June 1988)Google Scholar
  15. 15.
    Liu, Y., Anshus, O.J.: Improving the performance of vnc for high-resolution display walls. In: Proceedings of the 2009 International Symposium on Collaborative Technologies and Systems, pp. 376–383. IEEE Computer Society, Washington, DC, USA (2009)CrossRefGoogle Scholar
  16. 16.
    Manferdelli, J.L., Govindaraju, N.K., Crall, C.: Challenges and opportunities in many-core computing. Proceedings of the IEEE 96(5), 808–815 (2008)CrossRefGoogle Scholar
  17. 17.
    Microsoft, http://www.msdn.microsoft.com/en-us/library/aa383015(VS.85).aspxGoogle Scholar
  18. 18.
  19. 19.
  20. 20.
    Pande, V.S., Baker, I., Chapman, J., Elmer, S.P., Khaliq, S., Larson, S.M., Rhee, Y.M., Shirts, M.R., Snow, C.D., Sorin, E.J., Zagrovic, B.: Atomistic protein folding simulations on the submillisecond time scale using worldwide distributed computing. Biopolymers 68(1), 91–109 (2003)CrossRefGoogle Scholar
  21. 21.
  22. 22.
    Singh, R., Jeong, B., Renambot, L., Johnson, A., Leigh, J.: Teravision: a distributed, scalable, high resolution graphics streaming system. In: CLUSTER 2004: Proceedings of the 2004 IEEE International Conference on Cluster Computing, pp. 391–400 (2004)Google Scholar
  23. 23.
    Smarr, L.L., Chien, A.A., DeFanti, T., Leigh, J., Papadopoulos, P.M.: The optiputer. Commun. ACM 46(11), 58–67 (2003)CrossRefGoogle Scholar
  24. 24.
    Sodan, A.C., Machina, J., Deshmeh, A., Macnaughton, K., Esbaugh, B.: Parallelism via multithreaded and multicore cpus. Computer 43, 24–32 (2010)CrossRefGoogle Scholar
  25. 25.
    Stainforth, D., Kettleborough, J., Martin, A., Simpson, A., Gillis, R., Akkas, A., Gault, R., Collins, M., Gavaghan, D., Allen, M.: Climateprediction.net: Design principles for public-resource modeling research. In: 14th IASTED International Conference Parallel and Distributed Computing and Systems, pp. 32–38 (2002)Google Scholar
  26. 26.
  27. 27.
  28. 28.
    Vinter, B.: The architecture of the minimum intrusion grid (mig). In: Broenink, J.F., Roebbers, H.W., Sunter, J.P.E., Welch, P.H., Wood, D.C. (eds.) CPA. Concurrent Systems Engineering Series, vol. 63, pp. 189–201. IOS Press, Amsterdam (2005)Google Scholar
  29. 29.
    Wessels, D., Claffy, K., Braun, H.-W.: NLANR prototype Web caching system (1995), http://ircache.nlaur.net/
  30. 30.
    Zhang, C.: OptiStore: An On-Demand Data processing Middleware for Very Large Scale Interactive Visualization. PhD thesis, Computer Science, Graduate College of the University of Illinois, Chicago (2008)Google Scholar
  31. 31.
    Zhang, C., Leigh, J., DeFanti, T.A., Mazzucco, M., Grossman, R.: Terascope: distributed visual data mining of terascale data sets over photonic networks. Future Gener. Comput. Syst. 19(6), 935–943 (2003)CrossRefGoogle Scholar

Copyright information

© IFIP International Federation for Information Processing 2011

Authors and Affiliations

  • Tor-Magne Stien Hagen
    • 1
  • Daniel Stødle
    • 1
  • John Markus Bjørndalen
    • 1
  • Otto Anshus
    • 1
  1. 1.Department of Computer Science, Faculty of Science and TechnologyUniversity of TromsøNorway

Personalised recommendations