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The computational efficiency of a state of the art ab initio quantumtransport (QT) solver, capable of revealing the coupled electro-thermalproperties of atomically-resolved nano-transistors, has been improved by up totwo orders of magnitude through a data centric reorganization of theapplication. The approach yields coarse-and fine-grained data-movementcharacteristics that can be used for performance and communication modeling,communication-avoidance, and dataflow transformations. The resulting code hasbeen tuned for two top-6 hybrid supercomputers, reaching a sustainedperformance of 85.45 Pflop/s on 4,560 nodes of Summit (42.55% of the peak) indouble precision, and 90.89 Pflop/s in mixed precision. These computationalachievements enable the restructured QT simulator to treat realisticnanoelectronic devices made of more than 10,000 atoms within a 14$\times$shorter duration than the original code needs to handle a system with 1,000atoms, on the same number of CPUs/GPUs and with the same physical accuracy.

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