Improving Data Access for Computational Grid Applications |
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Authors: | Ron Oldfield David Kotz |
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Institution: | (1) Scalable Computing Systems, Sandia National Laboratories, Albuquerque, NM, P.O. Box 5800, 87185-1110;(2) Department of Computer Science, Dartmouth College, 6211 Sudikoff Laboratory, Hanover, NH, 03755 |
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Abstract: | High-performance computing increasingly occurs on “computational grids” composed of heterogeneous and geographically distributed
systems of computers, networks, and storage devices that collectively act as a single “virtual” computer. A key challenge
in this environment is to provide efficient access to data distributed across remote data servers. Our parallel I/O framework,
called Armada, allows application and data-set providers to flexibly compose graphs of processing modules that describe the
distribution, application interfaces, and processing required of the dataset before computation. Although the framework provides
a simple programming model for the application programmer and the data-set provider, the resulting graph may contain bottlenecks
that prevent efficient data access. In this paper, we present an algorithm used to restructure Armada graphs that distributes
computation and data flow to improve performance in the context of a wide-area computational grid.
This work was supported by Sandia National Laboratories under DOE contract DOE-AV6184.
Ron A. Oldfield is a senior member of the technical staff at Sandia National Laboratories in Albuquerque, NM. He received the B.Sc. in computer
science from the University of New Mexico in 1993. From 1993 to 1997, he worked in the computational sciences department of
Sandia National Laboratories, where he specialized in seismic research and parallel I/O. He was the primary developer for
the GONII-SSD (Gas and Oil National Information Infrastructure–Synthetic Seismic Dataset) project and a co-developer for the
R&D 100 award winning project “Salvo”, a project to develop a 3D finite-difference prestack-depth migration algorithm for
massively parallel architectures. From 1997 to 2003 he attended graduate school at Dartmouth college and received his Ph.D.
in June, 2003. In September of 2003, he returned to Sandia to work in the Scalable Computing Systems department. His research
interests include parallel and distributed computing, parallel I/O, and mobile computing.
David Kotz is a Professor of Computer Science at Dartmouth College in Hanover NH. After receiving his A.B. in Computer Science and Physics
from Dartmouth in 1986, he completed his Ph.D in Computer Science from Duke University in 1991. He returned to Dartmouth to
join the faculty in 1991, where he is now Professor of Computer Science, Director of the Center for Mobile Computing, and
Executive Director of the Institute for Security Technology Studies. His research interests include context-aware mobile computing,
pervasive computing, wireless networks, and intrusion detection. He is a member of the ACM, IEEE Computer Society, and USENIX
associations, and of Computer Professionals for Social Responsibility. For more information see http://www.cs.dartmouth.edu/dfk/. |
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