Parallel and Distributed Processing Symposium, International (2007)
Long Beach, CA, USA
Mar. 26, 2007 to Mar. 30, 2007
Micah Beck , University of Tennessee, Deptment of Computer Science, Knoxville, TN 37996-3450, USA. firstname.lastname@example.org
Huadong Liu , University of Tennessee, Deptment of Computer Science, Knoxville, TN 37996-3450, USA. email@example.com
Jian Huang , University of Tennessee, Deptment of Computer Science, Knoxville, TN 37996-3450, USA. firstname.lastname@example.org
Terry Moore , University of Tennessee, Deptment of Computer Science, Knoxville, TN 37996-3450, USA. email@example.com
To use heterogeneous and geographically distributed resources as a platform for parallel visualization is an intriguing topic of research. This is because of the immense potential impact of the work, and also because of its use of a full range of challenging technologies. In this work, we designed an execution environment for visualization of massive scientific datasets, using network functional units (NFU) for processing power, logistical networking for storage management and visualization cookbook library (vcblib) for visualization operations. This environment is based solely on computers distributed across the Internet that are owned and operated by independent institutions, while being openly shared for free. Those Internet computers are inherently of heterogeneous hardware configuration and running a variety of operating systems. Using 100 such processors, we have been able to obtain the same level of performance offered by a 64-node cluster of 2.2 GHz P4 processors, while processing a 75GBs subset of a cutting-edge simulation dataset. Due to its inherently shared nature, this execution environment for data-intensive visualization could provide a viable means of collaboration among geographically separated users.
J. Huang, T. Moore, M. Beck and H. Liu, "Scalable Distributed Execution Environment for Large Data Visualization," 2007 IEEE International Parallel and Distributed Processing Symposium(IPDPS), Rome, 2007, pp. 340.