ON-DEMAND HIGH-PERFORMANCE VISUALIZATION OF SPATIAL DATA ON HIGH-RESOLUTION TILED DISPLAY WALLS

Tor-Magne Stien Hagen, Daniel Stødle, Otto J. Anshus

2010

Abstract

Visualization of large data sets on high-resolution display walls is useful and can lead to new discoveries that would not have been noticeable on regular displays. However, exploring such data sets with interactive performance is challenging. This paper presents live data sets, a scalable architecture for visualization of large data sets on display walls. The architecture separates visualization systems from compute systems using a live data set containing data customized for the particular visualization domain. Experiments conducted show that the main bottleneck is the compute resources producing data for the visualization side. When all data is cached in the live data set, the main bottleneck (decoding images to create OpenGL textures and constructing geometry from raster data) is on the visualization side. On a 22 megapixel, 28 node display wall, the visualization system can decode 414.2 megapixels of images (19 frames) per second. However, the decoding is multi-threaded, and increased performance is expected using multi-core computers.

References

  1. Andrade, H., Kurc, T., Sussman, A., and Saltz, J. (2007). Active semantic caching to optimize multidimensional data analysis in parallel and distributed environments. Parallel Comput., 33(7-8):497-520.
  2. Beynon, M. D., Kurc, T., C¸ ataly ürek, U., Chang, C., Sussman, A., and Saltz, J. (2001). Distributed processing of very large datasets with datacutter. Clusters and computational grids for scientific computing, 27(11):1457-1478.
  3. Calit2 (2008). http://hiperwall.calit2.uci.edu/?q=node/1.
  4. Chang, F., Dean, J., Ghemawat, S., Hsieh, W. C., Wallach, D. A., Burrows, M., Ch, T., Fikes, A., and Gruber, R. E. (2006). Bigtable: A distributed storage system for structured data. In In Proceedings of the 7th Conference on USENIX Symposium on Operating Systems Design and Implementation - Volume 7, pages 205- 218.
  5. Correa, W. T., Klosowski, J. T., Morris, C. J., and Jackmann, T. M. (2007). SPVN: a new application framework for interactive visualization of large datasets. In SIGGRAPH 7807: ACM SIGGRAPH 2007 courses, page 6.
  6. Dean, J. and Ghemawat, S. (2008). Mapreduce: simplified data processing on large clusters. Commun. ACM, 51(1):107-113.
  7. ESRI (1997). http://www.esri.com/software/arcgis/.
  8. Ghemawat, S., Gobioff, H., and Leung, S. T. (2003). The google file system. SIGOPS Oper. Syst. Rev., 37(5):29-43.
  9. Humphreys, G., Houston, M., Ng, R., Frank, R., Ahern, S., Kirchner, P. D., and Klosowski, J. T. (2002). Chromium: a stream-processing framework for interactive rendering on clusters. In SIGGRAPH 7802: Proceedings of the 29th annual conference on Computer graphics and interactive techniques, pages 693-702.
  10. Jeong, B., Renambot, L., Jagodic, R., Singh, R., Aguilera, J., Johnson, A., and Leigh, J. (2006). Highperformance dynamic graphics streaming for scalable adaptive graphics environment. In SC 7806: Proceedings of the 2006 ACM/IEEE conference on Supercomputing, page 108.
  11. 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., and Williams, R. (2004). Accessing and visualizing scientific spatiotemporal data. In Scientific and Statistical Database Management, 2004. Proceedings. 16th International Conference on, pages 107-110.
  12. Kooima, R. (2008). Planetary-scale Terrain Composition. PhD thesis, Computer Science, Graduate College of the University of Illinois, Chicago.
  13. Kunz, A., (editors), J. D., Staadt, O., Walker, J., Nuber, C., and Hamann, B. (2003). A survey and performance analysis of software platforms for interactive clusterbased multi-screen rendering.
  14. Kurc, T., C¸ atalyürek, U., Chang, C., Sussman, A., and Saltz, J. (2001). Visualization of large data sets with the active data repository. IEEE Comput. Graph. Appl., 21(4):24-33.
  15. Li, P. (2002). Supercomputing visualization for earth science datasets. In Proceedings of 2002 NASA Earth Science Technology Conference.
  16. Li, P., Duquette, W. H., and Curkendall, D. W. (1996). Riva: A versatile parallel rendering system for interactive scientific visualization. IEEE Transactions on Visualization and Computer Graphics, 2(3):186-201.
  17. Liang, H., Arangarasan, R., and Theller, L. (2007). Dynamic visualization of high resolution gis dataset on multi-panel display using arcgis engine. Computers and Electronics in Agriculture, 58(2):174 - 188.
  18. Singh, R., Jeong, B., Renambot, L., Johnson, A., and Leigh, J. (2004). Teravision: a distributed, scalable, high resolution graphics streaming system. In CLUSTER 7804: Proceedings of the 2004 IEEE International Conference on Cluster Computing, pages 391-400.
  19. Smarr, L. L., Chien, A. A., DeFanti, T., Leigh, J., and Papadopoulos, P. M. (2003). The optiputer. Commun. ACM, 46(11):58-67.
  20. Taesombut, N., Wu, X. R., Chien, A. A., Nayak, A., Smith, B., Kilb, D., Im, T., Samilo, D., Kent, G., and Orcutt, J. (2006). Collaborative data visualization for earth sciences with the optiputer, future generation computer systems. Future Gener. Comput. Syst, 22:955- 963.
  21. Wessels, D., Claffy, K., and Braun, H.-W. (1995). NLANR prototype Web caching system, http://ircache.nlaur.net/.
  22. Williams, C. (2007). http://blog.irisink.com/2007/06/14/ display-wall-with-google-earth-on-mac-os-x/.
  23. Zhang, C. (2008). 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.
  24. Zhang, C., Leigh, J., DeFanti, T. A., Mazzucco, M., and Grossman, R. (2003). Terascope: distributed visual data mining of terascale data sets over photonic networks. Future Gener. Comput. Syst., 19(6):935-943.
Download


Paper Citation


in Harvard Style

Stien Hagen T., Stødle D. and J. Anshus O. (2010). ON-DEMAND HIGH-PERFORMANCE VISUALIZATION OF SPATIAL DATA ON HIGH-RESOLUTION TILED DISPLAY WALLS . In Proceedings of the International Conference on Imaging Theory and Applications and International Conference on Information Visualization Theory and Applications - Volume 1: IVAPP, (VISIGRAPP 2010) ISBN 978-989-674-027-6, pages 112-119. DOI: 10.5220/0002849601120119


in Bibtex Style

@conference{ivapp10,
author={Tor-Magne Stien Hagen and Daniel Stødle and Otto J. Anshus},
title={ON-DEMAND HIGH-PERFORMANCE VISUALIZATION OF SPATIAL DATA ON HIGH-RESOLUTION TILED DISPLAY WALLS},
booktitle={Proceedings of the International Conference on Imaging Theory and Applications and International Conference on Information Visualization Theory and Applications - Volume 1: IVAPP, (VISIGRAPP 2010)},
year={2010},
pages={112-119},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002849601120119},
isbn={978-989-674-027-6},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Imaging Theory and Applications and International Conference on Information Visualization Theory and Applications - Volume 1: IVAPP, (VISIGRAPP 2010)
TI - ON-DEMAND HIGH-PERFORMANCE VISUALIZATION OF SPATIAL DATA ON HIGH-RESOLUTION TILED DISPLAY WALLS
SN - 978-989-674-027-6
AU - Stien Hagen T.
AU - Stødle D.
AU - J. Anshus O.
PY - 2010
SP - 112
EP - 119
DO - 10.5220/0002849601120119