The Lattice Boltzmann Method (LBM) is a class of Computational Fluid Dynamics methods which models the fluid as fictive particles. In this paper, we report our work on SunwayLB, which enables LBM based solutions aiming for industrial applications using advanced heterogeneous systems such as the Sunway supercomputers. We propose several techniques to boost the simulation speed and improve the scalability of SunwayLB, including a customized multi-level domain decomposition and data sharing scheme, a carefully orchestrated strategy to fuse kernels with different performance constraints for a more balanced workload, and optimization strategies for assembly code. Based on these optimization schemes, we manage to scale SunwayLB on three advanced supercomputers: Sunway TaihuLight, the new Sunway Supercomputer and a GPU cluster. On Sunway TaihuLight, our largest simulation involves up to 5.6 trillion lattice cells, achieving 11,245 billion cell updates per second (GLUPS), 77% memory bandwidth utilization and a sustained performance of 4.7 PFlops. We further improve the memory bandwidth utilization and computational efficiency using the unique features of a new generation of Sunway supercomputer. On the new Sunway Supercomputer, the largest simulation contains over 4.2 trillion lattice cells, resulting in 6,583 GLUPS, 81% memory bandwidth utilization and a sustained performance of 2.76 PFlops. To evaluate the portability of our code, we also adapt our code to a GPU cluster with tailored optimization techniques, resulting in 191x speedup and 83.8% memory bandwidth utilization. We demonstrate a series of computational experiments for extreme-large scale fluid flow, as examples of real-world applications, to check the validity and performance of our work. The results show that our implementation is competent to be a highly scalable and efficient solution for large-scale CFD problems on heterogeneous systems.

The Lattice Boltzmann Method (LBM) has gained widespread popularity due to its applicability in fluid dynamics, chemical engineering, material science, and other domains. In this work, we present an optimized implementation of the LBM, with a specific focus on achieving superior performance and scalability on advanced heterogeneous systems such as the new Sunway supercomputer. To accomplish this, we employ several techniques, including kernel fusion to enhance temporal and spatial locality, a customized multi-level domain decomposition and data sharing scheme, and pipelining strategies that are tailored to the SW26010-Pro processor. As a result of these optimizations, we have successfully scaled our code to a total of 39,000,000 CPU cores. Our largest simulation, which encompassed over 42 trillion lattice cells, achieved an impressive 67,018 billion lattice cell updates per second (GLUPS), with 82.9% memory bandwidth utilization, and a sustained performance of 28 PFlops. In order to assess the portability of our implementation, we also adapted our code to run on a GPU cluster, utilizing a range of tailored optimization techniques. Our results demonstrated a 191x speedup, along with 83.8% memory bandwidth utilization. Our proposed approach marks a significant milestone in the field of LBM implementations, as it demonstrates unprecedented scalability by effectively utilizing over 39,000,000 cores while maintaining exceptional parallel efficiency and computational performance. This achievement establishes our method as a compelling solution for addressing large-scale computational fluid dynamics challenges on heterogeneous systems.

With the arrival of the Internet of Things (IoT) era, multi-tier computing has attracted significant attention. The multi-tier computing can organize many computing devices and provide sufficient computing resources to support various IoT applications. However, due to the complex architecture and the dynamic system status of the multi-tier computing network, task offloading for multi-tier computing is still challenging. This paper proposes a novel task offloading method named OSTTD, to deal with the Offloading of Splittable Tasks with Topological Dependence in multi-tier computing networks. OSTTD formulates the task offloading as a sequential decision-making problem and learns the task offloading policy by Deep Reinforcement Learning (DRL). Compared with existing task offloading approaches, OSTTD is the first method in which the topological dependence among sub-tasks of the splittable task is fully considered. OSTTD makes offloading decisions intelligently based on the dynamic system status and can be applied to various multi-tier network topology structures. To verify the effectiveness of OSTTD, we extend and build a latency-aware multi-tier computing simulation platform. Extensive simulations show that OSTTD can significantly reduce the task processing time, thus, improving the overall task processing efficiency in multi-tier computing networks.

The Lattice Boltzmann Method (LBM) is a relatively new class of Computational Fluid Dynamics methods. In this paper, we report our work on SunwayLB, which enables LBM based solutions aiming for industrial applications. We propose several techniques to boost the simulation speed and improve the scalability of SunwayLB, including a customized multi-level domain decomposition and data sharing scheme, a carefully orchestrated strategy to fuse kernels with different performance constraints for a more balanced workload, and optimization strategies for assembly code, which bring up to 137x speedup. Based on these optimization schemes, we manage to perform the largest direct numerical simulation which involves up to 5.6 trillion lattice cells, achieving 11,245 billion cell updates per second (GLUPS), 77% memory bandwidth utilization and a sustained performance of 4.7 PFlops. We also demonstrate a series of computational experiments for extreme-large scale fluid flow, as examples of real-world applications, to check the validity and performance of our work. The results show that SunwayLB is competent for a practical solution for industrial applications.