Parallel multigrid methods are widely used as preconditioned for solving large sparse linear systems. Most multigrids rely on general sparse matrix formats, which prevent them from achieving optimal performance. There is an emerging trend towards semi-structured multigrids that balance flexibility with performance. However, existing libraries often fall short in terms of speed and scalability for semi-structured problems. To address these limitations, we have designed and implemented Semi-StructMG. It employs multi-dimensional coarsening to reduce complexity and simplify communication patterns. It also considers the special role of inter-block connections in smoothers and triple-matrix products to improve convergence under large-scale parallelism. We evaluated Semi-StructMG using two benchmark problems and four real-world applications from petroleum reservoir simulation, ship manufacturing, numerical weather prediction, and ocean modeling. Compared to hypre's multigrids, Semi-StructMG achieves the fastest time-to-solution across all cases, with average speedups of 5.97x, 15.2x, and 3.85x over SSAMG, Split, and BoomerAMG, respectively. Additionally, Semi-StructMG significantly improves both strong and weak scaling efficiencies in all tests. These results suggest that it can serve as an effective alternative to SSAMG and Split.

This paper presents DAS-ILU, a Distributed Asynchronous parallel Incomplete LU factorization method based on domain decomposition. DAS-ILU partitions the computational domain into independently processed interior nodes and asynchronously updated separator nodes, thereby reducing cross-processor dependencies and halving the separator size compared to conventional methods. To further improve performance, it employs optimized data exchange patterns to minimize communication overhead and extends support to block-structured sparse matrices via exact block inversions. Comprehensive evaluations on a range of problem types—including structural mechanics, computational fluid dynamics, and reservoir simulation demonstrate the superior performance of DAS-ILU. Compared to state-of-the-art ILU implementations, DAS-ILU achieves solve time speedups of up to 2.07 × over Chow-Patel’s fine-grained parallel ILU and up to 4.11 × over HYPRE’s ILU. Moreover, DAS-ILU exhibits strong robustness when applied to challenging nonsymmetric and indefinite systems.

The efficiency of spatial queries is pivotal for the analysis of geometry data in the fields such as computational simulation, point cloud processing and digital engineering. Utilizing the computational capabilities of modern hardware, such as GPUs, offers a promising avenue for accelerating spatial query processing. However, conventional tree-based indexing methods are not optimized for maximal exploitation of GPU resources. To address this problem, we introduce BLAEQ, a multigrid index designed to maximize the potential of GPUs. BLAEQ adopts a multigrid strategy, which represents an index tree with vectors as layers and matrices as connectors. Although BLAEQ shares conceptual similarities with traditional tree-based indexes, its innovative multigrid architecture facilitates effective parallelization on GPUs during the query phase. To optimize GPU utilization, BLAEQ is entirely constructed using BLAS (Basic Linear Algebra Subprograms), leveraging the efficiency of hardware-tuned BLAS libraries like CuBLAS. This design confers BLAEQ with enhanced performance over existing spatial query methods. Our study assesses BLAEQ's performance against state-of-the-art spatial query techniques using a range of both real-world and synthetic datasets. The experimental outcomes demonstrate that BLAEQ outperforms the benchmark approaches in terms of query efficiency on geometry data.

Deoxyribonucleic acid(DNA) N6-methyladenine plays a vital role in various biological processes, and the accurate identification of its site can provide a more comprehensive understanding of its biological effects. There are several methods for 6mA site prediction. With the continuous development of technology, traditional techniques with the high costs and low efficiencies are gradually being replaced by computer methods. Computer methods that are widely used can be divided into two categories: traditional machine learning and deep learning methods. We first list some existing experimental methods for predicting the 6mA site, then analyze the general process from sequence input to results in computer methods and review existing model architectures. Finally, the results were summarized and compared to facilitate subsequent researchers in choosing the most suitable method for their work.

Color clustering is fluently exploited for many applications, especially in the fields of computer graphics and image processing. After studying the methods of color clustering, a new scheme based on ant colony clustering algorithm applied in cloth images is proposed in the paper. According to the pick up-drop theory, an improved ant algorithm is applied to group colors into certain clusters in the RGB space. Experimental result shows that the algorithm proposed in this paper has the advantages of an excellent robustness, a less time consumption, a simple realization and fitting for cloth image.