LAMMPS is a widely used molecular dynamics (MD) software package in materials science, computational chemistry, and biophysics, supporting parallel computing from a single CPU core to large supercomputers. The Kunpeng processor features both high memory bandwidth and core density and is therefore an interesting candidate for accelerating compute-intensive workloads. In this paper, we target the Kunpeng multi-core architecture and focus on optimizing LAMMPS for modern ARM-based platforms by using the Lennard-Jones (L-J) and Tersoff potentials as representative case studies. We investigate both common and specific optimization challenges, and present a comprehensive performance analysis addressing four key aspects: neighbor list algorithm design, force computation optimization, efficient vectorization, and multi-thread parallelization. Experimental results show that the optimized potentials achieve speedups of approximately$2 \times$and$5 \times$, reaching$4.55 \times$and$7.04\times$the performance of the original Intel version for L-J and Tersoff, respectively. Both potentials outperform Intel's acceleration library, with a peak performance up to$2.9\times$-$3.5\times$. In terms of parallel efficiency, we evaluate scalability both within a single CPU (small-scale) and across multiple nodes (large-scale). Strong and weak scaling tests within a single CPU show that when the expansion factor is 32 times, parallel efficiency remains above$90\%$. Large-scale weak scaling across multiple nodes achieves up to$86\%$efficiency when the expansion factor is 32. Using 32 nodes (18,432 processes), our implementation enables billion-atom simulations with L-J and Tersoff potentials. This work achieves breakthrough performance and provides critical support for large-scale molecular dynamics in engineering applications.

Leveraging the latest Sunway supercomputer, we developed a fully optimized earthquake simulation model that accurately captures topographic effects for realistic seismic analysis. Optimizing for the SW26010Pro architecture with DMA/RMA communication mechanisms, data compression schemes, and vectorization, we achieved a speedup exceeding 160×. Our pipeline-based computation and communication overlapping scheme, combined with performance prediction models further minimized computational costs. These optimizations enabled the largest-scale curvilinear grid finite-difference method (CGFDM) earthquake simulations to date, covering 197 trillion grid points and achieving 86.7 PFLOPS on 39 million cores with a weak scaling efficiency of 97.9%. These advancements enabled the successful simulation of the 2008 Wenchuan earthquake, providing high-resolution seismic insights and robust assessments for regional hazard mitigation and disaster preparedness.

Due to the superiority of handling irregular regions of interest, the curvilinear grid finite difference method (CGFDM) has become wildly used in seismic simulation for earthquake hazard evaluation and understanding of earthquake physics. This paper proposes a novel approach that optimizes a CGFDM solver on the Ascend, a cutting-edge Neural Processing(NPU) Unit using half-precision storage and mixed-precision arithmetic. The approach increases the data throughput and computing efficiency, enabling more effective seismic modeling. Furthermore, we propose an efficient matrix unit enabled 3D difference algorithm that employs matrix unit on NPU to accelerate the computation. By fully exploiting the capability of matrix unit and wide SIMD lane, our solver on Ascend achieves a speedup of 4.19 × over the performance of parallel solver on two AMD CPUs and has successfully simulated real-world Wenchuan earthquake. For the best of our knowledge, we are the first to conduct seismic simulations on NPU.

Molecular dynamics (MD) simulation can provide an affordable way for inspecting microscopic phenomena, which is a powerful complement to real-world experiments. But the spatial scale of MD simulations is usually magnitudes smaller than experiment systems. In this paper, we present our work, redesigning the widely used inter-layer potential in structural superlubricity. By carrying out a specialized neighbor list for inter-layer potential computation, the total memory access amount is reduced significantly. Besides, a simple but efficient vectorization strategy is implemented based on the new neighbor list. In the extreme case, our work can scale to 38 million cores to achieve a sustainable performance of 61 PFLOPS, enabling a simulation of a superlubricity system of 32 μm2 with 7.2 billion atoms at 4.75 ns/day, which is 11,834 times of reported largest scale simulation in superlubricity systems in contact area and almost ten times faster in time-to-solution. Furthermore, we have done a simulation at 9 μm2 which results in consistency with real-world experiments and verified some theoretical predictions in the mesoscopic scale.

A high-scalable and fully optimized earthquake model is presented based on the latest Sunway supercomputer. Contributions include: 1) the curvilinear grid finite-difference method (CGFDM) and flexible model applying perfectly matched layer (PML) and enabling more accurate and realistic terrain descriptions; 2) a hybrid and non-uniform domain decomposition scheme that efficiently maps the model across different levels of the computing system; and 3) sophisticated optimizations that largely alleviate or even eliminate bottlenecks in memory, communication, etc., obtaining a speedup of over 140×. Combining all innovations, the design fully exploits the hardware potential of all aspects and enables us to perform the largest CGFDM-based earthquake simulation ever reported (69.7 PFlops using over 39 million cores). Based on our design, the Turkey earthquakes (February 6, 2023), and the Ridgecrest earthquake (July 4, 2019), are successfully simulated with a maximum resolution of 12-m. Precise hazard evaluations for the hazardous reduction of earthquake-stricken areas are also conducted.

Molecular dynamics (MD) is one of the most crucial computer simulation methods for understanding real-world processes at the atomic level. Reactive potentials based on the bond order concept have the ability to model dynamic bond breaking and formation with close to quantum mechanical (QM) precision without actually requiring expensive QM calculations. In this article, we focus on the adaptive intermolecular reactive empirical bond-order (AIREBO) potential in LAMMPS for the simulation of carbon and hydrocarbon systems on the new Sunway supercomputer. To achieve scalable performance, we propose a parallel two-level building scheme and periodic buffering strategy for the tailored data design to explore data locality and data reuse. Furthermore, we design two optimized nearest-neighbor access algorithms: the redistribution of accumulated coefficients algorithm and the double-end search connectivity algorithm. Finally, we implement parallel force computation with an AoS data layout and hardware/software co-cache. In addition, we have designed a low-overhead atomic operation-based load balancing method and vectorization. The overall performance of AIREBO achieves a speedup of nearly$20\times$on a single core group (CG), and more than$5\times$and$4\times$over an Intel Xeon E5 2680 v3 core and an Intel Xeon Gold 6138 core, respectively. Compared with the Intel accelerator package in LAMMPS, our performance further achieves$3.0\times$of an Intel Xeon E5 2680 v3 core and is better than that of an Intel Xeon Gold 6138 core. We complete the validation of the results in no more than 20.5 hours on a single node with 2,000,000 running steps (i.e., 1 ns). Our experiments show that the simulation of 2,139,095,040 atoms on 798,720 ((1MPE+64CPEs) × 12,288 processes) cores exhibits a parallel efficiency of 88% under weak scaling.

Molecular dynamics (MD) simulations are playing an increasingly important role in many areas ranging from chemical materials to biological molecules. With the continuing development of MD models, the potentials are getting larger and more complex. In this article, we focus on the reactive force field (ReaxFF) potential from LAMMPS to optimize the computation of interactions. We present our efforts on refactoring for neighbor list building, bond order computation, as well as valence angles and torsion angles computation. After redesigning these kernels, we develop a vectorized implementation for non-bonded interactions, which is nearly 100 × faster than the management processing element (MPE) on the Sunway TaihuLight supercomputer. Furthermore, we have implemented the three-body-list free torsion angles computation, and propose a line-locked software cache method to eliminate write conflicts in the torsion angle and valence angle interactions resulting in an order-of-magnitude speedup on a single Sunway TaihuLight node. In addition, we achieve a speedup of up to 3.5 compared to the KOKKOS package on an Intel Xeon Gold 6148 core. When executed on 1,024 processes, our implementation enables the simulation of 21,233,664 atoms on 66,560 cores with a performance of 0.032 ns/day and a weak scaling efficiency of 95.71 percent.

LAMMPS is one of the most popular Molecular Dynamic (MD) packages and is widely used in the field of physics, chemistry and materials simulation. Layered Materials Force Field (LMFF) is our expansion of the LAMMPS potential function based on the Tersoff potential and inter-layer potential (ILP) in LAMMPS. LMFF is designed to study layered materials such as graphene and boron hexanitride. It is universal and does not depend on any platform. We have also carried out a series of optimizations on LMFF and the optimization work is carried out on the new generation of Sunway supercomputer, called SWLMFF. Experiments show that our implementation is efficient, scalable and portable. When generic LMFF is ported to Intel Xeon Gold 6278C, 2X performance improvement is achieved. For the optimized SWLMFF, the overall performance improvement is nearly 200--330X compared to the original ILP and Tersoff potentials. And SWLMFF has good parallel efficiency of 95%-100% under weak scaling with 2.7 million atoms on a single process. The maximum atomic system simulated by SWLMFF is close to 231 atoms. And nanosecond simulations in one day can be realized.

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.

Modern high throughput sequencing platforms can produce large amounts of short read DNA data at low cost. Error correction is an important but time-consuming initial step when processing this data in order to improve the quality of downstream analyses. In this paper, we present a Scalable Parallel Error CorrecToR designed to improve the throughput of DNA error correction for Illumina reads on various parallel platforms. Our design is based on a k-spectrum approach where a Bloom filter is frequently probed as a key operation and is optimized towards AVX-512-based multi-core CPUs, Xeon Phi many-cores (both KNC and KNL), and heterogeneous compute clusters. A number of architecture-specific optimizations are employed to achieve high performance such as memory alignment, vectorized Bloom filter probing, and a stack-based iteration to eliminate recursion. Our experiments show that our optimizations result in speedups of up to 2.8, 5.2, and 9.3 on a CPU (Xeon W-2123), a KNC-based Xeon Phi (31S1P), and a KNL-based Xeon Phi (7210), respectively, compared to a multi-threaded CPU reference implementation for the error correction stage. Furthermore, when executed on the same hardware, SPECTR achieves a speedup of up to 1.7, 2.1, 2.4, and 6.4, compared to the state-of-the-art tools Lighter, BLESS2, RECKONER, and Musket, respectively. In addition, our MPI implementation exhibits an efficiency of around 86% when executed on 32 nodes of the Tianhe-2 supercomputer. SPECTR is available at https://github.com/Xu-Kai/SPECTR.