Abstract
MD simulation in process engineering features enormous computational demands, and therefore requires efficient parallelization techniques. This chapter describes ls1 mardyn ’s parallelization approach for shared-memory and distributed-memory architectures. This is done by first defining today’s computing architectures and their governing design principles: Heterogeneity, massive amounts of cores and data parallelism. Based on this, we are then able to reengineer ls1 mardyn in such a way that it can optimally leverage important hardware features, and describe our parallelization approach for shared- and distributed-memory systems at the example of the Intel Xeon processor and the Intel Xeon Phi coprocessor, respectively. We close this section by describing load-balancing techniques in case of a distributed-memory parallelization and heterogeneous particle distributions in the computational domain.
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Heinecke, A., Eckhardt, W., Horsch, M., Bungartz, HJ. (2015). Parallelization of MD Algorithms and Load Balancing. In: Supercomputing for Molecular Dynamics Simulations. SpringerBriefs in Computer Science. Springer, Cham. https://doi.org/10.1007/978-3-319-17148-7_3
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DOI: https://doi.org/10.1007/978-3-319-17148-7_3
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