Automatic Visualization Pipeline Formation for Medical Datasets on Grid Computing Environment
Distance visualization of large datasets often takes the direction of remote viewing and zooming techniques of stored static images. However, the continuous increase in the size of datasets and visualization operation causes insufficient performance with traditional desktop computers. Additionally, the visualization techniques such as Isosurface depend on the available resources of the running machine and the size of datasets. Moreover, the continuous demand for powerful computing powers and continuous increase in the size of datasets results an urgent need for a grid computing infrastructure. However, some issues arise in current grid such as resources availability at the client machines which are not sufficient enough to process large datasets. On top of that, different output devices and different network bandwidth between the visualization pipeline components often result output suitable for one machine and not suitable for another. In this paper we investigate how the grid services could be used to support remote visualization of large datasets and to break the constraint of physical co-location of the resources by applying the grid computing technologies. We show our grid enabled architecture to visualize large medical datasets (circa 5 million polygons) for remote interactive visualization on modest resources clients.
[1] Bethel .W, Tierney. Brian, Lee. J, Gunter .D, Lau S (2000): Visapult
Using High-Speed WANs and Network Data Caches to Enable Remote
and Distributed Visualization, 2000 IEEE.
[2] Xiaoyu Zhang, Chandrajit Bajaj, William Blanke : 2001 Scalable
Isosurface Visualization of Massive Datasets on COTS Clusters :
Proceedings of the IEEE 2001 symposium on parallel and large-data
visualization and graphics.
[3] Engel K Sommer .O, Ernst C, Ertl T. (2000): Remote 3D Visualization
using Image- Streaming Techniques. 2000.
[4] Brett Beeson1,2, Mark Dwyer1, David 2005 : Server-side Visualization
of Massive Datasets Thompson3 Proceedings of the First International
Conference on e-Science and Grid Computing (e-Science-05).
[5] Foster, C. Kesselman, Nick .K. M., Tuecke .S (2002): The Physiology of
the Grid: An Open Grid Services Architecture for Distributed Systems
Integration. Technical report, Globus, February 2002.
[6] McCormick B. H., DeFanti T. A., Brown M. D. (1987), "Visualization in
Scientific Computing", Computer Graphics 21 1-14.
[7] Haber, R.B. and McNabb, D.A. 1990. Visualization Idioms: A
Conceptual Model for Scientific Visualization Systems. In:
Visualization in Scientific Computing, Shriver, B., Neilson, G.M., and
Rosenblum, L.J., Eds., IEEE Computer Society Press, 74-93.
[8] Upson, C., Faulhaber, T., Kamins, D., Schlegel, D., Laidlaw, D.,
Vroom, J., Gurwitz, R. and van Dam, A. 1989. The Application
Visualization System: a Computational Environment for Scientific
Visualization, IEEE Computer Graphics and Applications 9, 4, 30- 42.
[9] Will Schroeder, Ken Martin, and Bill Lorensen, The Visualization
Toolkit: An Object-Oriented Approach To 3D Graphics. Second Edition.
Prentice Hall. Upper Saddle River, NJ. 1998.
[10] SGI. SGI OpenGL VizServer 3.1. Data sheet, SGI, March 2003.
[11] Walton, J.P.R.B. (2004). NAG-s IRIS Explorer. In: Visualization
Handbook, Johnson, C.R. and Hansen, C.D., Eds., Academic Press (in
press). Available at http://www.nag.co.uk/doc/TechRep/Pdf/tr2_03.pdf
[12] Walker D. W. , Grimstead .I (2004): Resource aware visualization
environment. http://www.wesc.ac.uk/projects/rave/.2004
[13] Wood. J, Brodlie, K., J. Walton. (2003) gViz - visualization and
steering for the grid. In Proceedings of the UK All Hands Meeting 2003,
http://www.nesc.ac.uk/events/ahm2003/AHMCD/pdf/030.pdf.,
http://www.visualization.leeds.ac.uk/gViz.
[14] Charters, S., Holliman, N.S. and Munro, M. 2003. Visualization in e-
Demand: Grid Service Architecture for Stereoscopic Visualization,
Proceedings of UK e-Science Second All Hands Meeting.
[15] Osborne .J, Wright .H, (2003) SuperVise: Using Grid Tools to Support
Visualization. In Proceedings of the Fifth International Conference on
Parallel Processing and Applied Mathematics (PPAM 2003).
[16] Mahovsky .J, Benedicenti. L (2003): Architecture for Java-Based Real-
Time Distributed Visualization. IEEE Transactions on Visualization and
Computer Graphics, 9(4):570 - 579, October December 2003.
[17] Allen .G, Benger. W, Goodale. T, Hege H.-C, Lanfermann . G , Merzky .
A, Radke. T , Seidel .E, Shalf .J (2000): The Cactus Code: A Problem
Solving Environment for the Grid. In Proceedings of the Ninth
International Symposium on High Performance Distributed Computing
(HPDC-00), pages 253-262. IEEE, August 2000.
[18] Engel K. et al.. (2000): Combining Local and Remote Visualization
Techniques for Interactive Volume Rendering in Medical Applications.
2000.
[19] Lorensen, William and Harvey E. Cline. Marching Cubes: A High
Resolution 3D Surface Construction Algorithm. Computer Graphics
(SIGGRAPH 87 Proceedings) 21(4) July 1987, p. 163-170)
http://www.cs.duke.edu/education/courses/fall01/cps124/resources/p163
-lorensen.pdf
[20] Ade J. Fewings and Nigel W. John, "Distributed Graphics Pipelines on
the Grid," IEEE Distributed Systems Online, vol. 8, no. 1, 2007, art. no.
0701-o1001.
[21] Dutra, Rodrigues, Giraldi, Schulze, "Distributed Visualization Using
VTK in Grid Environments," ccgrid, pp. 381-388, Seventh IEEE
International Symposium on Cluster Computing and the Grid (CCGrid
'07), 2007.
[22] William J. Schroeder, Jonathan A. Zarge , William E. Lorensen,
Decimation of triangle meshes, ACM SIGGRAPH Computer Graphics,
v.26 n.2, p.65-70, July 1992.
[23] Thomas Sandholm and Jarek Gawor. Globus Toolkit 3 Core - A Grid
Service Container Framework. Globus Toolkit 3 Core White Paper, July
2003.
[24] M. L. Massie, B. N. Chun, and D. E. Culler, The Ganglia Distributed
Monitoring System: Design, Implementation, and Experience, Parallel
Computing, Vol. 30, Issue 7, July, 2004.
[25] Ian Bowman 2004 Performance Modeling for 3D Visualization in a
Heterogeneous Computing Environment: available online
http://vis.lbl.gov/Publications/2004/Bowman-PGV-LBNL-56977.pdf
[1] Bethel .W, Tierney. Brian, Lee. J, Gunter .D, Lau S (2000): Visapult
Using High-Speed WANs and Network Data Caches to Enable Remote
and Distributed Visualization, 2000 IEEE.
[2] Xiaoyu Zhang, Chandrajit Bajaj, William Blanke : 2001 Scalable
Isosurface Visualization of Massive Datasets on COTS Clusters :
Proceedings of the IEEE 2001 symposium on parallel and large-data
visualization and graphics.
[3] Engel K Sommer .O, Ernst C, Ertl T. (2000): Remote 3D Visualization
using Image- Streaming Techniques. 2000.
[4] Brett Beeson1,2, Mark Dwyer1, David 2005 : Server-side Visualization
of Massive Datasets Thompson3 Proceedings of the First International
Conference on e-Science and Grid Computing (e-Science-05).
[5] Foster, C. Kesselman, Nick .K. M., Tuecke .S (2002): The Physiology of
the Grid: An Open Grid Services Architecture for Distributed Systems
Integration. Technical report, Globus, February 2002.
[6] McCormick B. H., DeFanti T. A., Brown M. D. (1987), "Visualization in
Scientific Computing", Computer Graphics 21 1-14.
[7] Haber, R.B. and McNabb, D.A. 1990. Visualization Idioms: A
Conceptual Model for Scientific Visualization Systems. In:
Visualization in Scientific Computing, Shriver, B., Neilson, G.M., and
Rosenblum, L.J., Eds., IEEE Computer Society Press, 74-93.
[8] Upson, C., Faulhaber, T., Kamins, D., Schlegel, D., Laidlaw, D.,
Vroom, J., Gurwitz, R. and van Dam, A. 1989. The Application
Visualization System: a Computational Environment for Scientific
Visualization, IEEE Computer Graphics and Applications 9, 4, 30- 42.
[9] Will Schroeder, Ken Martin, and Bill Lorensen, The Visualization
Toolkit: An Object-Oriented Approach To 3D Graphics. Second Edition.
Prentice Hall. Upper Saddle River, NJ. 1998.
[10] SGI. SGI OpenGL VizServer 3.1. Data sheet, SGI, March 2003.
[11] Walton, J.P.R.B. (2004). NAG-s IRIS Explorer. In: Visualization
Handbook, Johnson, C.R. and Hansen, C.D., Eds., Academic Press (in
press). Available at http://www.nag.co.uk/doc/TechRep/Pdf/tr2_03.pdf
[12] Walker D. W. , Grimstead .I (2004): Resource aware visualization
environment. http://www.wesc.ac.uk/projects/rave/.2004
[13] Wood. J, Brodlie, K., J. Walton. (2003) gViz - visualization and
steering for the grid. In Proceedings of the UK All Hands Meeting 2003,
http://www.nesc.ac.uk/events/ahm2003/AHMCD/pdf/030.pdf.,
http://www.visualization.leeds.ac.uk/gViz.
[14] Charters, S., Holliman, N.S. and Munro, M. 2003. Visualization in e-
Demand: Grid Service Architecture for Stereoscopic Visualization,
Proceedings of UK e-Science Second All Hands Meeting.
[15] Osborne .J, Wright .H, (2003) SuperVise: Using Grid Tools to Support
Visualization. In Proceedings of the Fifth International Conference on
Parallel Processing and Applied Mathematics (PPAM 2003).
[16] Mahovsky .J, Benedicenti. L (2003): Architecture for Java-Based Real-
Time Distributed Visualization. IEEE Transactions on Visualization and
Computer Graphics, 9(4):570 - 579, October December 2003.
[17] Allen .G, Benger. W, Goodale. T, Hege H.-C, Lanfermann . G , Merzky .
A, Radke. T , Seidel .E, Shalf .J (2000): The Cactus Code: A Problem
Solving Environment for the Grid. In Proceedings of the Ninth
International Symposium on High Performance Distributed Computing
(HPDC-00), pages 253-262. IEEE, August 2000.
[18] Engel K. et al.. (2000): Combining Local and Remote Visualization
Techniques for Interactive Volume Rendering in Medical Applications.
2000.
[19] Lorensen, William and Harvey E. Cline. Marching Cubes: A High
Resolution 3D Surface Construction Algorithm. Computer Graphics
(SIGGRAPH 87 Proceedings) 21(4) July 1987, p. 163-170)
http://www.cs.duke.edu/education/courses/fall01/cps124/resources/p163
-lorensen.pdf
[20] Ade J. Fewings and Nigel W. John, "Distributed Graphics Pipelines on
the Grid," IEEE Distributed Systems Online, vol. 8, no. 1, 2007, art. no.
0701-o1001.
[21] Dutra, Rodrigues, Giraldi, Schulze, "Distributed Visualization Using
VTK in Grid Environments," ccgrid, pp. 381-388, Seventh IEEE
International Symposium on Cluster Computing and the Grid (CCGrid
'07), 2007.
[22] William J. Schroeder, Jonathan A. Zarge , William E. Lorensen,
Decimation of triangle meshes, ACM SIGGRAPH Computer Graphics,
v.26 n.2, p.65-70, July 1992.
[23] Thomas Sandholm and Jarek Gawor. Globus Toolkit 3 Core - A Grid
Service Container Framework. Globus Toolkit 3 Core White Paper, July
2003.
[24] M. L. Massie, B. N. Chun, and D. E. Culler, The Ganglia Distributed
Monitoring System: Design, Implementation, and Experience, Parallel
Computing, Vol. 30, Issue 7, July, 2004.
[25] Ian Bowman 2004 Performance Modeling for 3D Visualization in a
Heterogeneous Computing Environment: available online
http://vis.lbl.gov/Publications/2004/Bowman-PGV-LBNL-56977.pdf
@article{"International Journal of Information, Control and Computer Sciences:51499", author = "Aboamama Atahar Ahmed and Muhammad Shafie Abd Latiff and Kamalrulnizam Abu Bakar and Zainul AhmadRajion", title = "Automatic Visualization Pipeline Formation for Medical Datasets on Grid Computing Environment", abstract = "Distance visualization of large datasets often takes the direction of remote viewing and zooming techniques of stored static images. However, the continuous increase in the size of datasets and visualization operation causes insufficient performance with traditional desktop computers. Additionally, the visualization techniques such as Isosurface depend on the available resources of the running machine and the size of datasets. Moreover, the continuous demand for powerful computing powers and continuous increase in the size of datasets results an urgent need for a grid computing infrastructure. However, some issues arise in current grid such as resources availability at the client machines which are not sufficient enough to process large datasets. On top of that, different output devices and different network bandwidth between the visualization pipeline components often result output suitable for one machine and not suitable for another. In this paper we investigate how the grid services could be used to support remote visualization of large datasets and to break the constraint of physical co-location of the resources by applying the grid computing technologies. We show our grid enabled architecture to visualize large medical datasets (circa 5 million polygons) for remote interactive visualization on modest resources clients.
", keywords = "Visualization, Grid computing, Medical datasets, visualization techniques, thin clients, Globus toolkit, VTK.", volume = "1", number = "7", pages = "1894-6", }
