Jiajie Zhang, Shulin Cao, Tingjian Zhang, Xin Lv, Juanzi Li, Lei Hou, Jiaxin Shi, Qi Tian. Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2023.

Molecular dynamics (MD) simulations are playing an increasingly important role in many research areas. Pair-wise potentials are widely used in MD simulations of bio-molecules, polymers, and nano-scale materials. Due to a low compute-to-memory-access ratio, their calculation is often bounded by memory transfer speeds. Sunway TaihuLight is one of the fastest supercomputers featuring a custom SW26010 many-core processor. Since the SW26010 has some critical limitations regarding main memory bandwidth and scratchpad memory size, it is considered as a good platform to investigate the optimization of pair-wise potentials especially in terms of data reusage. MD algorithms often use a neighbor-list data structure to reduce the computational workload. In this paper, we show that a cell-list-based approach is more suitable for the SW26010 processor. We apply a number of novel optimization methods including self-adaptable replica-summation for conflict-free parallelization, parameter profiles for flexible vectorization, and particle-cell cutoff checking filters for reducing the computational workload. We also established an open source standalone framework featuring the techniques above, ESMD1, which is at least 50% faster than the latest existing LAMMPS port on a single TaihuLight node. Furthermore, EMSD achieves a weak scaling efficiency of 88% on 4,096 nodes.

Molecular dynamics (MD) simulations are playing an increasingly important role in several research areas. The most frequently used potentials in MD simulations are pair-wise potentials. Due to the memory wall, computing pair-wise potentials on many-core processors are usually memory bounded. In this paper, we take the SW26010 processor as an exemplary platform to explore the possibility to break the memory bottleneck by improving data reusage via cell-list-based methods. We use cell-lists instead of neighbor-lists in the potential computation, and apply a number of novel optimization methods. Theses methods include: an adaptive replica arrangement strategy, a parameter profile data structure, and a particle-cell cutoff checking filter. An incremental cell-list building method is also realized to accelerate the construction of cell-lists. Furthermore, we have established an open source standalone framework, ESMD, featuring the techniques above. Experiments show that ESMD is 50~170% faster than previous ports on a single node, and can scale to 1,024 nodes with a weak scalibility of 95%.

Large-scale molecular dynamics (MD) simulations on supercomputers play an increasingly important role in many research areas. With the capability of simulating charge equilibration (QEq), bonds and so on, Reactive force field (ReaxFF) enables the precise simulation of chemical reactions. Compared to the first principle molecular dynamics (FPMD), ReaxFF has far lower requirements on computational resources so that it can achieve higher efficiencies for large-scale simulations. In this article, we present our efforts on scaling ReaxFF on the Sunway TaihuLight Supercomputer (TaihuLight). We have carefully redesigned the force analysis and neighbor list building steps. By applying fine-grained optimizations we gain better single process performance. For the many-body interactions, we propose an isolated computation and update strategy and implement inverse trigonometric functions. For QEq, we implement a pipelined conjugate gradient (CG) approach to achieving better scalability. Furthermore, we reorganize the data layout and implement the update operation based on data locality in ReaxFF. Our experiments show that this approach can simulate chemical reactions with 1,358,954,496 atoms using 4,259,840 cores with a performance of 0.015 ns/day. To our best knowledge, this is the first realization of chemical reaction simulation with a millimeter-scale force field.

GROMACS is one of the most popular Molecular Dynamic (MD) applications and is widely used in the field of chemical and bimolecular system study. Similar to other MD applications, it needs long run-time for large-scale simulations. Therefore, many high performance platforms have been employed to accelerate it, such as Knights Landing (KNL), Cell Processor, Graphics Processing Unit (GPU) and so on. As the third fastest supercomputer in the world, Sunway TaihuLight contains 40960 SW26010 processors and SW26010 is a typical many-core processor. To make full use of the superior computation ability of TaihuLight, we port GROMACS to SW26010 with following new strategies: (1) a new deferred update strategy; (2) a new update mark strategy; (3) a full pipeline acceleration. Furthermore, we redesign GROMACS to enable all possible vectorization. Experiments show that our implementation achieves better performance than both Intel KNL and Nvidia P100 GPU when using appropriate number of SW26010 processors for a fair comparison.

This paper reports our large-scale nonlinear earthquake simulation software on Sunway TaihuLight. Our innovations include: (1) a customized parallelization scheme that employs the 10 million cores efficiently at both the process and the thread levels; (2) an elaborate memory scheme that integrates on-chip halo exchange through register communcation, optimized blocking configuration guided by an analytic model, and coalesced DMA access with array fusion; (3) on-the-fly compression that doubles the maximum problem size and further improves the performance by 24%. With these innovations to remove the memory constraints of Sunway TaihuLight, our software achieves over 15% of the system's peak, better than the 11.8% efficiency achieved by a similar software running on Titan, whose byte to flop ratio is 5 times better than TaihuLight. The extreme cases demonstrate a sustained performance of over 18.9 Pflops, enabling the simulation of Tangshan earthquake as an 18-Hz scenario with an 8-meter resolution.