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.

Genomic structural inversion is a class of structural variations, and has been widely associated to a series of complex traits and diseases. It has great significance in accurately identifying the inversions from the high-throughput sequencing data for both research and clinical practice. However, detecting inversion is a challenging computational problem. Existing approaches either limit to detect the inversions with specific length intervals or require a significant distribution of the coverage across the candidate interval. In this paper, we propose a novel detection algorithm to accurately identify the inversions with wide-spectrum of length. The proposed algorithm consists of two components: a clustering step and a segmentation and extension step. It first clusters the pair-ended reads to squeeze the candidate intervals. Then, it utilizes the contig assembly strategy to reconstruct the candidate intervals. Meanwhile, a segmentation and extension strategy is implemented. For each candidate interval, a feature vector is calculated, based on the characteristic values. Finally, the algorithm combines the comparison verification results to filter out some potential false positives, and then returns the inversion breakpoints on base-pair resolution. We conduct a series of simulation experiments to verify the performance of proposed algorithm and compare to two very popular approaches, DELLY and Pindel. The results demonstrate that the proposed approach provides better results on handling the inversions with wide-spectrum of length, especially when the inversions with short-to-medium length exist.