Abstract
Ab initio electronic-structure has remained dichotomous between achievable accuracy and length-scale. Quantum many-body (QMB) methods realize quantum accuracy but fail to scale. Density functional theory (DFT) scales favorably but remains far from quantum accuracy. We present a framework that breaks this dichotomy by use of three interconnected modules: (i) invDFT: a methodological advance in inverse DFT linking QMB methods to DFT; (ii) MLXC: a machine-learned density functional trained with invDFT data, commensurate with quantum accuracy; (iii) DFT-FE-MLXC: an adaptive higher-order spectral finite-element (FE) based DFT implementation that integrates MLXC with efficient solver strategies and HPC innovations in FE-specific dense linear algebra, mixed-precision algorithms, and asynchronous compute-communication. We demonstrate a paradigm shift in DFT that not only provides an accuracy commensurate with QMB methods in ground-state energies, but also attains an unprecedented performance of 659.7 PFLOPS (43.1% peak FP64 performance) on 619,124 electrons using 8,000 GPU nodes of Frontier supercomputer.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2023 |
| Publisher | Association for Computing Machinery, Inc |
| ISBN (Electronic) | 9798400701092 |
| DOIs | |
| State | Published - Nov 12 2023 |
| Event | 2023 International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2023 - Denver, United States Duration: Nov 12 2023 → Nov 17 2023 |
Publication series
| Name | Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2023 |
|---|
Conference
| Conference | 2023 International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2023 |
|---|---|
| Country/Territory | United States |
| City | Denver |
| Period | 11/12/23 → 11/17/23 |
Funding
We acknowledge our collaboration and discussions with Wenhao Sun and Woohyean Baek on the science of quasicrystals. V.G. and P.M.Z. acknowledge the support from DOE-BES (DE-SC0022241) under the auspices of which the computational framework connecting QMB methods and DFT was developed. V.G. and S.D. acknowledge DOE-BES (DE-SC0008637) for supporting the development of DFT-FE and the study of the energetics of extended defects in Mg alloys. B.K. acknowledges support from Toyota Research Institute that funded initial development and implementation of inverse DFT. P.M. and G.P. acknowledge the support from the Department of Science and Technology India (Startup research grant SRG/2020/002194) and the Ministry of Education India (Prime Minister’s Research Fellowship) for the development of GPU matrix-free frameworks employed in the electrostatics treatment of DFT calculations. V.G. also acknowledges AFOSR (FA9550-21-1-0302) that supported mathematical analysis of inverse degenerate eigenvalue problems. This work used resources of the Oak Ridge Leadership Computing Facility at the Oak Ridge National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725. This work also used resources of the National Energy Research Scientific Computing Center, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231.
Keywords
- density functional theory
- exascale computing
- finite elements
- heterogeneous architectures
- inverse problems
- lightweight alloys
- machine learning
- mixed precision
- quantum simulation
- quasicrystals
- scalability