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Issue No.12 - Dec. (2012 vol.18)
pp: 2285-2294
ABSTRACT
This paper presents the first volume visualization system that scales to petascale volumes imaged as a continuous stream of high-resolution electron microscopy images. Our architecture scales to dense, anisotropic petascale volumes because it: (1) decouples construction of the 3D multi-resolution representation required for visualization from data acquisition, and (2) decouples sample access time during ray-casting from the size of the multi-resolution hierarchy. Our system is designed around a scalable multi-resolution virtual memory architecture that handles missing data naturally, does not pre-compute any 3D multi-resolution representation such as an octree, and can accept a constant stream of 2D image tiles from the microscopes. A novelty of our system design is that it is visualization-driven: we restrict most computations to the visible volume data. Leveraging the virtual memory architecture, missing data are detected during volume ray-casting as cache misses, which are propagated backwards for on-demand out-of-core processing. 3D blocks of volume data are only constructed from 2D microscope image tiles when they have actually been accessed during ray-casting. We extensively evaluate our system design choices with respect to scalability and performance, compare to previous best-of-breed systems, and illustrate the effectiveness of our system for real microscopy data from neuroscience.
INDEX TERMS
virtual storage, data acquisition, data visualisation, electron microscopes, microscopy, neuroscience, interactive volume exploration, petascale microscopy data streams, visualization-driven virtual memory, volume visualization system, petascale volumes, continuous stream, high resolution electron microscopy image, anisotropic petascale volume, decouples construction, 3D multiresolution representation, data acquisition, multiresolution hierarchy, multiresolution virtual memory architecture, octree, microscopes, visible volume data, volume ray casting, cache misses, 3D blocks, 2D microscope image tiles, ray-casting, system design, best-of-breed system, real microscopy data, Data visualization, Neuroscience, Image resolution, Microscopy, Graphics processing unit, Octrees, Rendering (computer graphics), neuroscience, Petascale volume exploration, high-resolution microscopy, high-throughput imaging
CITATION
M. Hadwiger, J. Beyer, Won-Ki Jeong, H. Pfister, "Interactive Volume Exploration of Petascale Microscopy Data Streams Using a Visualization-Driven Virtual Memory Approach", IEEE Transactions on Visualization & Computer Graphics, vol.18, no. 12, pp. 2285-2294, Dec. 2012, doi:10.1109/TVCG.2012.240
REFERENCES
[1] J. Beyer, M. Hadwiger, J. Schneider., W.-K. Jeong, and H. Pfister., Distributed terascale volume visualization using distributed shared virtual memory. In Posters at Large-Data Analysis and Visualization 2011, 2011.
[2] B. Bilodeau, G. Sellers, and K. Hillesland. AMD GPU techni-cal publications: Partially resident textures (PRT) in the Graphics Core Next. http://developer.amd.com/documentation/presentations/ GPUTechnicalPublications, 2012. Accessed on 31/03/ 2012.
[3] I. Boada, I. Navazo, and R. Scopigno, Multiresolution Volume Visualization with a Texture-Based Octree The Visual Computer, 17: 185-197, 2001.
[4] D. Bock, W.-C. Lee, A. Kerlin., M. Andermann, G. Hood., A. Wetzel, S. Yurgenson., E. Soucy, H. S. Kim,, and R. C. Reid., Network anatomy and in vivo physiology of visual cortical neurons. Nature, 471(7337): 177-182, 2011.
[5] H. Childs, M. Duchaineau, and K.-L. Ma., A scalable, hybrid scheme for volume rendering massive data sets. In Eurographics Symposium on Parallel Graphics and Visualization, pages 153-162, 2006.
[6] C. Crassin, F. Neyret, S. Lefebvre,, and E. Eisemann., Gigavoxels: Ray-guided streaming for efficient and detailed voxel rendering. In Proceedings of 2009 Symposium on Interactive 3D Graphics and Games, pages 15-22, 2009.
[7] K. Engel., CERA-TVR: A framework for interactive high-quality teravoxel volume visualization on standard PCs. In Posters at Large-Data Analysis and Visualization 2011, 2011.
[8] T. Fogal, H. Childs, S. Shankar., J. Kruger, R. D. Bergeron,, and P. Hatcher., Large data visualization on distributed memory multi-GPU clusters. In Proceedings of High. Performance Graphics 2010, pages 57-66, 2010.
[9] T. Foley and J. Sugerman., Kd-tree acceleration structures for a GPU ray-tracer. In Proceedings of Graphics Hardware 2005, pages 15-22, 2005.
[10] E. Gobbetti, F. Marton, and J. Guitan, A single-pass gpu ray casting framework for interactive out-of-core rendering of massive volumetric datasets The Visual Computer, 24(7): 797-806, 2008.
[11] S. Guthe and W. Strasser, Advanced Techniques for High-Quality Multi-Resolution Volume Rendering Computers & Graphics, 28: 51-58, 2004.
[12] P. Heckbert, Survey of texture mapping IEEE Computer Graphics and Applications, 6(11): 56-67, 1986.
[13] J. L. Hennessey and D. A. Patterson, Computer Architecture: A Quantitative Approach. Morgan Kaufmann, fifth edition, 2011.
[14] K. E. Hoff, J. Keyser, M. Lin., D. Manocha, and T. Culver., Fast computation of generalized Voronoi diagrams using graphics hardware. In Proceedings of SIGGRAPH, pages 277-286, 1999.
[15] D. Horn, J. Sugerman, M. Houston,, and P. Hanrahan., Interactive k-d tree GPU raytracing. In Proceedings of Interactive 3D Graphics and Games 2007, 2007.
[16] M. Howison,E. W. Bethel,, and H. Childs., MPI-hybrid Parallelism for Volume Rendering on Large, Multi-core Systems. In Eurographics Symposium on Parallel Graphics and Visualization (EGPGV), pages 1-10, 2010.
[17] D. M. Hughes and I. S. Lim., Kd-jump: a path-preserving stackless traversal for faster isosurface ray tracing on GPUs IEEE Transactions on Visualization and Computer Graphics, 15(6): 1555-1562, 2009.
[18] M. Kraus and T. Ertl., Adaptive texture maps. In Proceedings of Graphics Hardware 2002, 2002.
[19] J. Krüger and T. Fogal., Tuvok - an architecture for large scale volume rendering. In Proceedings of the 15th Vision, Modeling and Visualization Workshop 2010, 2010.
[20] J. Krüger and R. Westermann., Acceleration Techniques for GPU-based Volume Rendering. In Proc. of IEEE Visualization, pages 287-292, 2003.
[21] E. LaMar, B. Hamann, and K. Joy., Multiresolution Techniques for Interactive Texture-Based Volume Visualization. In Proc. of IEEE Visualization, pages 355-362, 1999.
[22] A. Lefohn, S. Sengupta, and J. Owens, Resolution-matched shadow maps ACM Transactions on Graphics, 26(4): 1-23, 2007.
[23] J. W. Lichtman and W. Denk., The big and the small: Challenges of imaging the brain's circuits Science, 334(6056): 618-623, 2011.
[24] T. Lindeberg and L. Bretzner., Real-time scale selection in hybrid multi-scale representations. Technical report, KTH (Royal Institute of Technology), 2003.
[25] G. Morton., A computer oriented geodetic data base and a new technique in file sequencing. Technical report, IBM Ltd., 1966.
[26] T. Peterka, H. Yu, R. Ross,, and K.-L. Ma., Parallel volume rendering on the IBM Blue Gene/P. In Eurographics/ACM SIGGRAPH Symposium on Parallel Graphics and Visualization, 2008.
[27] S. Popov, J. Gunther, H.-P. Seidel,, and P. Slusallek., Stackless kd-tree traversal for high performance gpu ray tracing. Computer Graphics Fo-rum, 26(3), 2007.
[28] B. Summa, G. Scorzelli, M. Jiang,P.- T. Bremer,, and V. Pascucci., Interactive editing of massive imagery made simple: Turning Atlanta into Atlantis. ACM Transactions on Graphics, 30(2): 7:1-7:13, 2010.
[29] C. C. Tanner,C.J. Migdal,, and M. T. Jones., The clipmap: a virtual mipmap. In Proceedings of SIGGRAPH ‘98, pages 151–158. ACM, 1998.
[30] J. M. P. van Waveren., id tech 5 challenges: From texture virtualization to massive parallelization. Talk in Beyond Programmable Shading course, SIGGRAPH 2009, 2009.
[31] M. Weiler, R. Westermann, C. Hansen., K. Zimmerman, and T. Ertl., Level-Of-Detail Volume Rendering via 3D Textures. In Proc. of IEEE Symposium on Volume Visualization, pages 7-13, 2000.
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