Toward an extreme-scale electronic structure system.

Electronic structure calculations have the potential to predict key matter transformations for applications of strategic technological importance, from drug discovery to material science and catalysis. However, a predictive physicochemical characterization of these processes often requires accurate quantum chemical modeling of complex molecular systems with hundreds to thousands of atoms. Due to the computationally demanding nature of electronic structure calculations and the complexity of modern high-performance computing hardware, quantum chemistry software has historically failed to operate at such large molecular scales with accuracy and speed that are useful in practice. In this paper, novel algorithms and software are presented that enable extreme-scale quantum chemistry capabilities with particular emphasis on exascale calculations. This includes the development and application of the multi-Graphics Processing Unit (GPU) library LibCChem 2.0 as part of the General Atomic and Molecular Electronic Structure System package and of the standalone Extreme-scale Electronic Structure System (EXESS), designed from the ground up for scaling on thousands of GPUs to perform high-performance accurate quantum chemistry calculations at unprecedented speed and molecular scales. Among various results, we report that the EXESS implementation enables Hartree-Fock/cc-pVDZ plus RI-MP2/cc-pVDZ/cc-pVDZ-RIFIT calculations on an ionic liquid system with 623 016 electrons and 146 592 atoms in less than 45 min using 27 600 GPUs on the Summit supercomputer with a 94.6% parallel efficiency.

Q-Next: A Fast, Parallel, and Diagonalization-Free Alternative to Direct Inversion of the Iterative Subspace.

As computer systems dedicated to scientific calculations become massively parallel, the poor parallel performance of the Fock matrix diagonalization becomes a major impediment to achieving larger molecular sizes in self-consistent field (SCF) calculations. In this Article, a novel, highly parallel, and diagonalization-free algorithm for the accelerated convergence of the SCF procedure is presented. The algorithm, called Q-Next, draws on the second-order SCF, quadratically convergent SCF, and direct inversion of the iterative subspace (DIIS) approaches to enable fast convergence while replacing the Fock matrix diagonalization SCF bottleneck with higher parallel efficiency matrix multiplications. Performance results on both parallel multicore CPU and GPU hardware for a variety of test molecules and basis sets are presented, showing that Q-Next achieves a convergence rate comparable to the DIIS method while being, on average, one order of magnitude faster.