This paper presents the methods for decomposition and the performance of the Perfect Benchmark suite on two VAX multiprocessors. Our results indicate that by several efficient decomposition techniques we were able to ...
ISBN:
(纸本)9780897914123
This paper presents the methods for decomposition and the performance of the Perfect Benchmark suite on two VAX multiprocessors. Our results indicate that by several efficient decomposition techniques we were able to obtain significant performance gains on both VAX multiprocessors relative to a uniprocessor case. We propose a methodology that can be applied for decomposing other existing scientific and engineering applications and provide guidelines for auto-decomposing compiler improvements. Finally, we discuss and quantify the effect of different cache designs on the multiprocessor performance.
The authors present the methods for decomposition and the performance of the Perfect Benchmark suite on two VAX multiprocessors. Results indicate that it was possible to obtain significant performance gains on both VA...
详细信息
The authors present the methods for decomposition and the performance of the Perfect Benchmark suite on two VAX multiprocessors. Results indicate that it was possible to obtain significant performance gains on both VAX multiprocessors, relative to a uniprocessor case. A methodology that can be applied to decompose other existing scientific and engineering applications is proposed. Guidelines for autodecomposing compiler improvements are provided. The authors discuss and quantify the effect of different cache designs on the multiprocessor performance.< >
The authors describe the decomposition of six algorithms: two partial differential equations (PDE) solvers (successive over-relaxation (SOR) and alternating direction implicit (ADI)), fast Fourier transform (FFT), Mon...
详细信息
The authors describe the decomposition of six algorithms: two partial differential equations (PDE) solvers (successive over-relaxation (SOR) and alternating direction implicit (ADI)), fast Fourier transform (FFT), Monte Carlo simulations, simplex linear programming, and sparse solvers. They present the performance results of these algorithms on two shared-memory VAX/VMS multiprocessor prototypes: VAX 6300 series with up to eight processors and M31 with up to 22 processors. It is demonstrated that by efficient decomposition it is possible to achieve high performance for all algorithms on both prototypes. The efficient decomposition techniques applied to optimize the performance of parallel algorithms are described. The performance implications of different cache designs for two multiprocessors are discussed.< >
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