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Future Generation Computer Systems
Volume 22, Issue 8, October 2006, Pages 1032-1039
 
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doi:10.1016/j.future.2006.03.017    How to Cite or Link Using DOI (Opens New Window)
Copyright © 2006 Published by Elsevier B.V.

Real-time multi-scale brain data acquisition, assembly, and analysis using an end-to-end OptIPuter

Rajvikram Singhb, Corresponding Author Contact Information, E-mail The Corresponding Author, Nicholas Schwarzb, Nut Taesombutc, David Leea, Byungil Jeongb, Luc Renambotb, Abel W. Lina, Ruth Westa, Hiromu Otsukae, Sei Naitof, Steven T. Peltiera, Maryann E. Martonea, Kazunori Nozakid, Jason Leighb and Mark H. Ellismana

aNational Center for Microscopy and Imaging Research, University of California, San Diego, United States bElectronic Visualization Laboratory, University of Illinois at Chicago, United States cDepartment of Computer Science and Engineering, University of California, San Diego, United States dCybermedia Center, Osaka University, Japan eKDDI Corporation, Garden Air Tower, 10-10, Iidabashi 3-chome, Chiyoda-ku, Tokyo 102-8460, Japan fKDDI Labs, 2-1-15 Ohara, Fujimino, Saitama, 356-8502, Japan

Available online 9 May 2006.

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Abstract

At iGrid 2005 we demonstrated the transparent operation of a biology experiment on a test-bed of globally distributed visualization, storage, computational, and network resources. These resources were bundled into a unified platform by utilizing dynamic lambda allocation, high bandwidth protocols for optical networks, a Distributed Virtual Computer (DVC) [N. Taesombut, A. Chien, Distributed Virtual Computer (DVC): Simplifying the development of high performance grid applications, in: Proceedings of the Workshop on Grids and Advanced Networks, GAN 04, Chicago, IL, April 2004 (held in conjunction with the IEEE Cluster Computing and the Grid (CCGrid2004) Conference)], and applications running over the Scalable Adaptive Graphics Environment (SAGE) [L. Renambot, A. Rao, R. Singh, B. Jeong, N. Krishnaprasad, V. Vishwanath, V. Chandrasekhar, N. Schwarz, A. Spale, C. Zhang, G. Goldman, J. Leigh, A. Johnson, SAGE: The Scalable Adaptive Graphics Environment, in: Proceedings of WACE 2004, 23–24 September 2004, Nice, France, 2004]. Using these layered technologies we ran a multi-scale correlated microscopy experiment [M.E. Maryann, T.J. Deerinck, N. Yamada, E. Bushong, H. Ellisman Mark, Correlated 3D light and electron microscopy: Use of high voltage electron microscopy and electron tomography for imaging large biological structures, Journal of Histotechnology 23 (3) (2000) 261–270], where biologists imaged samples with scales ranging from 20X to 5000X in progressively increasing magnification. This allows the scientists to zoom in from entire complex systems such as a rat cerebellum to individual spiny dendrites. The images used spanned multiple modalities of imaging and specimen preparation, thus providing context at every level and allowing the scientists to better understand the biological structures. This demonstration attempts to define an infrastructure based on OptIPuter components which would aid the development and design of collaborative scientific experiments, applications and test-beds and allow the biologists to effectively use the high resolution real estate of tiled displays.

Keywords: Graphics clusters; Multi-scale correlated microscopy; Montage images; HDTV streaming; Telescience; Tile displays; Tiled displays; Optical networks; Scientific visualization; Remote instrumentation

Article Outline

1. Introduction
1.1. The challenge
1.2. OptIPuter
1.3. Layers in the OptIPuter
1.3.1. Lightpath provisioning using Photonic Inter-domain Negotiator (PIN)
1.3.2. Distributed Virtual Computer (DVC)
1.3.3. LambdaRAM
1.3.4. LambdaStream
1.3.5. Group Transport Protocol (GTP)
1.3.6. Scalable Adaptive Graphics Environment (SAGE)
1.3.7. Application layer
2. Microscopy experiments over OptIPuter
3. iGrid demonstration
4. Conclusion and future work
Acknowledgements
References
Vitae





 
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