Abstract
Many applications from scientific computing are computationally intensive and can therefore benefit from a realization on a parallel or a distributed platform. The parallelization strategy and the resulting efficiency strongly depends on the characteristics of the target architecture (shared address space or distributed address space) and the programming model used for the implementation. For selected problems from scientific computing, we discuss parallelization strategies using message-passing programming for distributed address space.
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Rauber, T., Rünger, G. 13. Parallel Implementation Strategiesfor Algorithms from Scientific Computing. In: Hergert, W., Däne, M., Ernst, A. (eds) Computational Materials Science. Lecture Notes in Physics, vol 642. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39915-5_13
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DOI: https://doi.org/10.1007/978-3-540-39915-5_13
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