Abstract
The main driving force for higher supercomputer performance is the fact that some important applications in engineering and science currently consume excessive amounts of time or are infeasible to attempt at all on available vector processors. To describe physical phenomena, one must resort to simulation of complex models on the computer. The closer the model is to a physical phenomenon, the more extensive are the required computational resources. Important uses of parallel processors are in the simulation of gauge theory and elementary particle physics, multidimensional semiconductor devices, electronic circuits, weather circulation, and oil reservoirs, as well as studies in chemical quantum dynamics and molecular scattering, seismic imaging and dynamic structural analysis.
This work was supported in part by the National Selence Foundation under Grants No. US NSF DCRS.-lOllO and US NSF DCRS5-09970, the US Department 01 EnerlY under Grant No. US DOE DE-FG02-85ER2500l, the AIr Force Office 01 Scientific Research under Grant No. AFOSR-85-0211, and the IBM Donation.
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Gallivan, K.A., Sameh, A.H. (1988). Matrix Computations on Shared-Memory Multiprocessors. In: Denham, M.J., Laub, A.J. (eds) Advanced Computing Concepts and Techniques in Control Engineering. NATO ASI Series, vol 47. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-83548-3_11
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