By J.J. Dongarra, G.R. Joubert, L. Grandinetti and J. Kowalik (Eds.)
ISBN-10: 0444821635
ISBN-13: 9780444821638
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Additional info for High Performance Computing: Technology, Methods and Applications
Sample text
The figure shows the computational efficiency (speedup divided by the number of processors) as a function of p (the number of processors) for the NAS multigrid benchmark. To calculate this efficiency curve we assume that the 8processor configuration of SP-1 is a fully efficient base system (efficiency = 1). ht to sixty-four processors to solve faster the fixed-size benchmark problem. Such behavior is not unusual and all of the benchmark tests is the NASA report show similar trends, except the embarrassingly parallel benchmark.
The issue of problem scaling merits further comments since large problems can be constructed in different ways. Singh, Hennessy and Gupta [ 1993] point out that computer scientists tend to think about algorithm complexity and problem size in terms of data set size. This may be correct for typical nonnumerical applications, but is too simplistic for most of the numerical analysis problems. In numerical applications we can scale problems by varying: grid size, time steps, accuracy of the model or numerical results, and other parameters.
While problems associated with building powerful processors and large shared memories make coarse grain systems relatively expensive, their low interprocessor communication overhead, high I/O bandwidth and mature software make these systems efficient performers for general purpose applications. Advances in processor chip technologies imply that medium grain systems have today's highest total peak performance ratings. Their software is getting more nature and such systems can be very cost effective for applications that can explore data locality or where the dominant communication pattern is to the nearest neighbor.
High Performance Computing: Technology, Methods and Applications by J.J. Dongarra, G.R. Joubert, L. Grandinetti and J. Kowalik (Eds.)
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