HPL-MxP is an emerging high performance benchmark used to measure the mixed-precision computing capability of leading supercomputers. In this work, we present our efforts on the new Sunway that linearly scales the benchmark to over 40 million cores, sustains an overall mixed-precision performance exceeding 5 ExaFlop/s, and achieves over 85% of peak performance, which is the highest efficiency reached among all heterogeneous systems on the HPL-MxP list. The optimizations of our HPL-MxP implementation include the following: (1) a Two-Direction Look-Ahead and Overlap algorithm that enables overlaps of all communications with computation; (2) a multi-level process-mapping and communication scheduling method that uses the entire network as best as possible while maintaining conflict-free algorithm-flow; and (3) a CG-Fusion computing framework that eliminates up to 60% of inter-chip communications and removes the memory access bottleneck while serving both computation and communication simultaneously. This work could also provide useful insights for tuning cutting-edge applications on Sunway supercomputers as well as other heterogeneous supercomputers.

The atomic kinetic Monte Carlo method plays an important role in multi-scale physical simulations because it bridges the micro and macro worlds. However, its accuracy is limited by empirical potentials. We therefore propose herein a triple-encoding algorithm and vacancy-cache mechanism to efficiently integrate ab initio neural network potentials (NNPs) with AKMC and implement them in our TensorKMC codes. We port our program to SW26010-pro and innovate a fast feature operator and a big fusion operator for the NNPs for fully utilizing the powerful heterogeneous computing units of the new-generation Sunway supercomputer. We further optimize memory usage. With these improvements, TensorKMC can simulate up to 54 trillions of atoms and achieve excellent strong and weak scaling performance up to 27,456,000 cores.

Raman spectroscopy provides chemical and compositional information that can serve as a structural fingerprint for various materials. Therefore, simulations of Raman spectra, including both quantum perturbation analyses and ground-state calculations are of significant interest. However, highly accurate full quantum mechanical (QM) simulations of Raman spectra have previously been confined to small systems. For large systems such as biological materials, the computational cost of full QM simulations is extremely high, and their extension to such systems remains challenging. In the work described here, by employing robust new algorithms and advances in implementation for the many-core architectures, we are able to perform fast, accurate, and massively parallel full ab initio simulations of the Raman spectra of biological systems with excellent strong and weak scaling, thereby providing a starting point for applying QM approaches to structural studies of such systems.