Accelerating Hybrid DFT Simulations Using Performance Modeling on Supercomputers

Density Functional Theory (DFT) is an electronic-structure theory that computes the electronic energy of atoms and molecules from their electron density. Among several DFT methods, one called “hybrid DFT” adds the Hartree-Fock exchange energy to the original DFT exchange energy, and it improves the accuracy of the estimation of energy. However, this introduces additional computational costs, preventing its wide application for large-scale calculations. In light of those issues, a performance model to tune the computational configurations for hybrid DFT software automatically is proposed. The proposed model makes it possible to exhaustively search for parameters to minimize computation time without having to execute actual calculations with all parameter combinations. Several techniques for optimizing hybrid DFT, specially designed for the Fugaku supercomputer, are also proposed. It is concluded that combining all approaches reduces node-time cost by 2.23x and 2.68x for a 52-atom input on Fugaku and ABCI, respectively.