Abstract
Numerical simulation in physics, chemistry and engineering sciences, as for instance in fluid dynamics can be grouped in two classes: Continuum models and many-body-models. The mathematical approximative methods used are numerical grid methods, molecular dynamics, Monte Carlo methods etc. The more complicate the considered phenomenon and the more refined the model is, the higher is the demand for computational power and storage capacity. Future high performance computers will be parallel machines in order to be able to safisfy the users of large numerical applications. Appropriate parallel architectures in particular of the multiple-instruction-multiple-data type (MIMD) are discussed in view of the mapping requirements and varying subtask structure of the considered numerical applications. Two distributed memory architectures are presented in more detail: SUPRENUM, a German supercomputer project and the Erlangen multiprocessor architecture. The SUPRENUM prototype, based on the message-passing communication principle, will consist of 256 processors with a theoretical overall peak performance of 2 GFLOPS. The Erlangen architectural concept is characterized by interprocessor communication via distributed shared memory (DSM) and a functional hierarchy of 3 levels. This multiprocessor architecture adapts especially well to the mapping requirements of most numerical simulation problems. This is due to the fact that DSM architectures match efficiently the local communication needs of the considered problem classes.
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Fritsch, G., Volkert, J. (1989). Multiprocessor systems for large numerical applications. In: Wolf, G., Legendi, T., Schendel, U. (eds) Parcella '88. Parcella 1988. Lecture Notes in Computer Science, vol 342. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-50647-0_117
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DOI: https://doi.org/10.1007/3-540-50647-0_117
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