Alexandros Nikolaos Ziogas, Tal Ben-Nun, Guillermo Indalecio Fernández, Timo Schneider, Mathieu Luisier, Torsten Hoefler:
A Data-Centric Approach to Extreme-Scale Ab initio Dissipative Quantum Transport Simulations
(In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (SC19), Nov. 2019) Won ACM Gordon Bell Prize
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 cen-
tric 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 sus-
tained 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 restruc-
tured 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.
@inproceedings{, author={Alexandros Nikolaos Ziogas and Tal Ben-Nun and Guillermo Indalecio Fernández and Timo Schneider and Mathieu Luisier and Torsten Hoefler}, title={{A Data-Centric Approach to Extreme-Scale Ab initio Dissipative Quantum Transport Simulations}}, year={2019}, month={Nov.}, booktitle={Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (SC19)}, source={http://www.unixer.de/~htor/publications/}, }
