Materials research is an area that is expected to strongly benefit from the growing performance capabilities of future supercomputers towards exascale. Density functional theory (DFT) has become one of the most important methods for numerical materials science. In this paper we present results of a performance model based analysis of a particular, scalable DFT-based application on GPU-accelerated compute nodes with POWER8 processors. These technologies are part of a future roadmap for pre-exascale architectures. With power consumption becoming a major design constraint, we also determine the energy required for executing the most performance critical kernel.
CITATION STYLE
Baumeister, P. F., Bornemann, M., Bühler, M., Hater, T., Krill, B., Pleiter, D., & Zeller, R. (2016). Addressing materials science challenges using GPU-accelerated POWER8 nodes. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9833 LNCS, pp. 77–89). Springer Verlag. https://doi.org/10.1007/978-3-319-43659-3_6
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