Particle-in-Cell (PIC) simulations devote most cycles to particle-grid interactions, and their fine-grained atomic updates become a severe bottleneck on traditional many-core CPUs. The evolution of CPU architectures, particularly the integration of specialized Matrix Processing Units (MPUs) designed for efficient matrix outer-product operations, presents a paradigm shift and an opportunity to alleviate these bottlenecks. Capitalizing on this architectural advancement, this work focuses on adapting the critical current deposition step in PIC simulations to this new matrix-centric computational model.

Particle-in-Cell (PIC) simulations are fundamental to plasma physics but often suffer from limited scalability due to particle–grid interaction bottlenecks and particle redistribution costs. Specifically, the particle–grid interaction computations have not taken full advantage of the emerging Matrix Processing Units (MPUs), the particle motion introduces irregular memory accesses, and the bulk-synchronous redistribution further destroys long-term data locality thereby limiting parallel efficiency. To address these inefficiencies, we present POLAR-PIC, a co-designed framework for large-scale PIC simulations that (i) reformulates Field Interpolation into an MPU-friendly outer-product form, (ii) maintains a physically ordered particle layout to preserve memory contiguity, and (iii) overlaps particle communication with Deposition to hide redistribution overhead. The evaluation on the pilot system of an Exascale supercomputer demonstrates that POLAR-PIC accelerates the entire particle-processing phase by up to 10.9 × in uniform plasma and 4.4 × in real-world laser-ion acceleration scenarios compared to the native WarpX reference pipeline on LX2. Ablation studies reveal that the speedups achieved by Interpolation and Deposition are 8.0 × and 13.2 × , respectively, and the asynchronous communication design sustains a \(99.1\%\) overlap ratio. In cross-platform comparisons, POLAR-PIC achieves \(13.2\%\) of theoretical peak efficiency on the CPU-based LS system, while WarpX reaches \(9.6\%\) on NVIDIA A800 GPUs. Notably, the scalability evaluation demonstrates that POLAR-PIC maintains \(67.5\%\) weak scaling efficiency on over 2 million cores under high-migration dynamic workloads, highlighting the importance of holistic co-design for future matrix-centric HPC systems.

Track planning for drones has been a common problem. In order to ensure that the UAV can fly long distance in accordance with the predetermined path, it is necessary to set calibration points on the flight path to correct the sensor errors of the UAV. This problem can be abstracted into a path planning problem and solved by ant colony algorithm. Considering the uncertainty of correction failure in these calibration points. This paper presents an improved ant colony algorithm and set up the “Enhanced pheromone volatilization strategy“ to ensure that the UAV could reach the destination with the greatest possibility in this uncertain situation. We verify our algorithm on public data sets**. On data set 1, our algorithm has a 100% probability of reaching the destination, while the traditional ant colony algorithm has only a 61% probability of reaching the destination. On data set 2, our algorithm has a 56% probability of reaching the destination, while the traditional ant colony algorithm cannot find a path can reach the destination. The algorithm code*** in this paper is simple to implement, strong robustness, and can be extended to other scenarios.