  The computational efficiency of a state of the art ab initio quantum
transport (QT) solver, capable of revealing the coupled electro-thermal
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$\times$
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.
