Abstract
Processing-in-memory architectures promise increased computing performance at decreased costs in energy, as the physical proximity of the compute pipelines to the data store eliminates overheads for data transport. We assess the overall performance impact using a recently introduced architecture of that type, called the Active Memory Cube, for two representative scientific applications. Precise performance results for performance critical kernels are obtained using cycle-accurate simulations. We provide an overall performance estimate using performance models.
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Acknowledgements
We thank the AMC team at IBM Research, in particular J. Moreno, for sharing their knowledge on the AMC and continued help on this project including many fruitful discussions. Furthermore, we gratefully acknowledge F.S. Schifano and R. Tripiccione (INFN/University of Ferrara) for making a mini-application version of their D2Q37 code available and for discussing their future roadmaps [18]. We also thank G. Koutsou, S. Krieg, and H. Simma from the Simulation Lab LQCD at Cyprus Institute/DESY/JSC for discussing the future requirements of LQCD [13]. Finally, we thank A. Frommer and S. Krieg for making their implementation of their AMG solver [8] available.
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Baumeister, P.F. et al. (2015). Accelerating LBM and LQCD Application Kernels by In-Memory Processing. In: Kunkel, J., Ludwig, T. (eds) High Performance Computing. ISC High Performance 2015. Lecture Notes in Computer Science(), vol 9137. Springer, Cham. https://doi.org/10.1007/978-3-319-20119-1_8
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DOI: https://doi.org/10.1007/978-3-319-20119-1_8
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