Rehosting A Mini-Computer Model On A Supercomputer

Many ecosystem simulation computer codes have been developed in the last twenty-five years. This development took place initially on main-frame computers, then mini-computers, and more recently, on micro-computers and workstations. Supercomputing platforms (both parallel and distributed systems) hav...

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Veröffentlicht in:Simulation (San Diego, Calif.) Calif.), 1996-01, Vol.66 (1), p.43-58
Hauptverfasser: Skiles, J.W., Schulbach, C.H.
Format: Artikel
Sprache:eng
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Zusammenfassung:Many ecosystem simulation computer codes have been developed in the last twenty-five years. This development took place initially on main-frame computers, then mini-computers, and more recently, on micro-computers and workstations. Supercomputing platforms (both parallel and distributed systems) have been largely unused, however, because of the perceived difficulty in accessing and using the machines. Also, significant differences in the system architectures of sequential, scalar computers and parallel and/or vector supercomputers must be considered. We have transferred a grassland simulation model (developed on a VAX) to a Cray Y-MP C90. We describe porting the model to the Cray and the changes we made to exploit the parallelism in the application and improve code execution. The Cray executed the model 30 times faster than the VAX and 10 times faster than a Unix workstation. We achieved an additional speedup of 30 percent by using the compiler's vectorizing and "in-line" capabilities. The code runs at only about 5 percent of the Cray's peak speed because it ineffectively uses the vector and parallel processing capabilities of the Cray. We expect that by restructuring the code, it could execute an additional six to ten times faster. Our goal was not just to increase the speed of code execution, but to enable the restructured and ported code to access and manipulate large data matrices holding intermediate and state variables, to increase the size of the geographical areas that can be simulated, and to be able to use large remote sensing data sets to drive the model or use as validation.
ISSN:0037-5497
1741-3133
DOI:10.1177/003754979606600105