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 coarseand fine-grained data-movement characteristics that can be used for performance and communication modeling, communicationavoidance, 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.

Designing efficient cooling systems for integrated circuits (ICs) relies on a deep understanding of the electro-thermal properties of transistors. To shed light on this issue in currently fabricated Fin-FETs, a quantum mechanical solver capable of revealing atomicallyresolved electron and phonon transport phenomena from firstprinciples is required. In this paper, we consider a global, datacentric view of a state-of-the-art quantum transport simulator to optimize its execution on supercomputers. The approach yields coarseand fine-grained data-movement characteristics, which are used for performance and communication modeling, communicationavoidance, and data-layout transformations. The transformations are tuned for the Piz Daint and Summit supercomputers, where each platform requires different caching and fusion strategies to perform optimally. The presented results make ab initio device simulation enter a new era, where nanostructures composed of over 10,000 atoms can be investigated at an unprecedented level of accuracy, paving the way for better heat management in next-generation ICs.

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As an intermediate step in a semiconductor device simulation framework, we write a multipole method to evaluate the electrostatic potential in the boundary nodes induced by the conduction electrons. A method initially designed to solve the nbody problem could not be the best choice, but as we have found, the results are excellent, obtaining a speed up of about 300 at some cases, and even 600 at the best situation, compared with the classical method using one core. Also, the method offers small errors and we find a good opportunity to optimize the algorithm in future works.