SC19 Proceedings

The International Conference for High Performance Computing, Networking, Storage, and Analysis

A Data-Centric Approach to Extreme-Scale Ab Initio Dissipative Quantum Transport Simulations

Authors: Alexandros Nikolaos Ziogas (ETH Zurich), Tal Ben-Nun (ETH Zurich), Guillermo Indalecio Fernandez (ETH Zurich), Timo Schneider (ETH Zurich), Mathieu Luisier (ETH Zurich), Torsten Hoefler (ETH Zurich)

Abstract: The computational efficiency of a state of the art ab initio quantum transport (QT) solver, capable of revealing the coupled electrothermal properties of atomically-resolved nano-transistors, has been improved by up to two orders of magnitude through a data centric reorganization of the application. The approach yields coarse- and fine-grained data-movement characteristics that can be used for performance and communication modeling, communication avoidance, and dataflow transformations. The resulting code has been tuned for two top-6 hybrid supercomputers, reaching a sustained performance of 85.45 Pflop/s on 4,560 nodes of Summit (42.55% of the peak) in double precision, and 90.89 Pflop/s in mixed precision. These computational achievements enable the restructured QT simulator to treat realistic nanoelectronic devices made of more than 10,000 atoms within a 14× shorter duration than the original code needs to handle a system with 1,000 atoms, on the same number of CPUs/GPUs and with the same physical accuracy.

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