FEATURES | |
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Foreword |
| By Thomas Zacharia Computation is becoming an equal partner with theory and experimentation in the advancement of science. | |
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The Future of High-Performance Computing |
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By Richard F. Sincovec The World Wide Web is expected to give computational researchers enhanced capabilities for retrieving and visualizing data and for solving problems. | |
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| The Center for Computational Sciences: High-Performance Computing Comes to ORNL |
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By Kenneth Kliewer The CCS focuses on solving Grand Challenge problems by linking powerful parallel computers together and by developing parallel applications codes and algorithms. | |
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| Industrial-Strength Computing: ORNL's Computational Center for Industrial Innovation |
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By Harvey Gray CCII is helping researchers from U.S. industrial firms, government agencies, and universities harness parallel computing to solve complex industrial problems. | |
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| Scientific Visualization at ORNL |
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By Ross Toedte and Dianne Wooten Better insight into scientific phenomena is the goal of ORNL's Visualization Laboratory, which uses visualization and virtual reality techniques and the latest software and hardware. | |
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| Visualization and Virtual Environments Research |
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By Raymond E. Flanery, Jr., Nancy W. Grady, Joel W. Reed, and Daniel R. Tufano ORNL's Advanced Visualization Research Center has explored climate modeling, seismic modeling, melting simulations, data mining, and three-dimensional medical imaging. | |
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| Algorithms, Tools, and Software Aid Use of High-Performance Computers |
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By Ed D'Azevedo, Jack Dongarra, Tom Dunigan, Al Geist, Chuck Romine, and Pat Worley ORNL and the University of Tennessee are leaders in developing tools for using high-performance computers efficiently. | |
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| Software Components To Facilitate Application Development |
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By Jack Dongarra, Noel Nachtigal, Esmond Ng, Barry Peyton, Bill Shelton, and David Walker ORNL researchers have developed computationally efficient linear algebra packages that are portable across different computer architectures. These tools have led to fast simulations of use to materials scientists and the automotive and aerospace industries. | |
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| High-Performance Computing: Innovative Assistant to Science |
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By James Arthur Kohl An ORNL computational tool developed for scientists is CUMULVS, which helps them simulate experiments and change the parameters in midcourse to influence the results, saving time and money. | |
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| Computing the Genome |
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By Edward Uberbacher ORNL is helping to design and prepare to implement a new computational engine to rapidly analyze large-scale genomic sequences. The goal is to keep up with the flood of data from the Human Genome Project. | |
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| Developing a Grand Challenge Materials Application Code |
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By William A. Shelton and G. Malcolm Stocks Computational methods and parallel processors enable scientists to simulate magnetic and other properties of metallic alloys based on the electronic structure of thousands of atoms. | |
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| How Solids Melt: ORNL Simulations Support Theory |
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By Mark Mostoller, Ted Kaplan, and Kun Chen Using parallel computing, ORNL scientists obtained insights into how substances melt in two dimensions and confirmed that a phase between the solid and liquid phases exists. | |
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| Giant Magnetoresistance in Layered Magnetic Materials |
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By Bill Butler, Xiaoguang Zhang, and Don Nicholson By using advanced computational techniques, ORNL scientists are learning how and why electrical resistivity varies in thin layers of magnetic alloys. This understanding could lead to improved magnetic memory for computers and motion sensors for appliances. | |
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| Edge Dislocations in Silicon |
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By Mark Mostoler, Ted Kaplan, and Matt Chisholm Using parallel computing, ORNL scientists inferred that edge dislocations in silicon that have no dangling bonds and no segregated impurities may be electrically or optically active. | |
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| High-Performance Computing in Groundwater Modeling |
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By Laura Toran, J. Gwo, G. Mahinthakumar, E. D'Azevedo, and C. Romine Computationally intense models that describe concentrations and movements of specific contaminants in groundwater may be more effective than simple groundwater models in evaluating remediation options. | |
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| Analysis of Material Performance in Automotive Applications |
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By Srdan Simunovic, Gustavo Aramayo, and Thomas Zacharia Computer simulations of collisions involving vehicles made of materials lighter than steel offer a quicker, cheaper way to guide the design of future vehicles to make them safer as well as more efficient. | |
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| Optimization of Microstructure—Property Relationship in Materials |
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By Balasubramaniam Radhakrishnan, Gorti Sarma, and Thomas Zacharia An ORNL computer model could be used to develop the optimum processing window for producing controlled texture in aluminum to minimize scrap from the manufacture of aluminum beverage cans. | |
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| Computational Engine Modeling |
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By Osman Yasar ORNL has adapted an engine combustion code for use on parallel computers, enabling faster designs of highly efficient and environmentally friendly cars. | |
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| From a Distance: Remote Operation of Research Equipment |
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By Carolyn Krause ORNL has received DOE 2000 funding to conduct experiments involving collaboration by electronic means among geographically separated researchers, including the remote operation of research equipment and development of electronic notebooks. | |
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| Crisis Management and Collaborative Computing: ORNL's Contributions |
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By Kimberly Barnes, John Cobb, and Nenad Ivezic ORNL researchers have been combining software engineering, computational science, and collaborative technologies to solve problems in enterprise management, disaster management, education, banking and finance, and engineering design and manufacturing. | |
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