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Programming tools and environments

Programming tools and environments Joel Saltz, Alan Sussman, Susan Graham, James Demmel, Scott Baden, and Jack Dongarra Programming Tools Environments Along with new languages, numerical libraries, and infrastructures ”with names like KeLP, Titanium, and Meta-Chaos ”they let scientists create deeply complex compute- and data-intensive applications. Neuron simulation rendered with the MCell program. (Courtesy, Tom Bartol, Salk Institute for Biological Studies, and Joel Stiles, Cornell University.) and November 1998/Vol. 41, No. 11 COMMUNICATIONS OF THE ACM Advances in the computational capabilities of high-performance architectures make it possible for computational scientists and engineers to address increasingly challenging problems. At the same time, it is becoming considerably more difficult to build software that achieves high performance on these systems. Programs addressing large-scale scientific problems often use sophisticated numerical techniques employing sparse or hierarchical data structures. Solving these important problems may require coupling several independently developed application codes, some using very large data sets consisting of empirical or simulated data. The challenge for researchers developing programming tools and environments for high-performance computing is to enable application programmers to more easily develop software systems that exploit contemporary architectures, while scaling up through the physical aspects of the problem, including problem size, data set size and complexity, the http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Communications of the ACM Association for Computing Machinery

Programming tools and environments

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Publisher
Association for Computing Machinery
Copyright
Copyright © 1998 by ACM Inc.
ISSN
0001-0782
DOI
10.1145/287831.287841
Publisher site
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Abstract

Joel Saltz, Alan Sussman, Susan Graham, James Demmel, Scott Baden, and Jack Dongarra Programming Tools Environments Along with new languages, numerical libraries, and infrastructures ”with names like KeLP, Titanium, and Meta-Chaos ”they let scientists create deeply complex compute- and data-intensive applications. Neuron simulation rendered with the MCell program. (Courtesy, Tom Bartol, Salk Institute for Biological Studies, and Joel Stiles, Cornell University.) and November 1998/Vol. 41, No. 11 COMMUNICATIONS OF THE ACM Advances in the computational capabilities of high-performance architectures make it possible for computational scientists and engineers to address increasingly challenging problems. At the same time, it is becoming considerably more difficult to build software that achieves high performance on these systems. Programs addressing large-scale scientific problems often use sophisticated numerical techniques employing sparse or hierarchical data structures. Solving these important problems may require coupling several independently developed application codes, some using very large data sets consisting of empirical or simulated data. The challenge for researchers developing programming tools and environments for high-performance computing is to enable application programmers to more easily develop software systems that exploit contemporary architectures, while scaling up through the physical aspects of the problem, including problem size, data set size and complexity, the

Journal

Communications of the ACM – Association for Computing Machinery

Published: Nov 1, 1998

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