Autonomous vehicle technology, underpinned by advanced artificial intelligence(AI), has become increasingly critical in modern engineering systems. To address the limitations of conventional A* algorithms specifically their computational inefficiency and limited robustness in indoor unmanned vehicle path planning this paper presents an enhanced adaptive A* algorithm driven by AI methodologies. The proposed approach incorporates four key innovations: heuristic function optimization through the integration of an obstacle risk coefficient and turning angle cost to improve path safety and smoothness; dynamic weight adjustment via an exponential decay function to balance search efficiency and solution quality; parent node optimization using recursive cost computation to minimize redundant calculations; and child node pruning to eliminate hazardous neighboring nodes, thereby preventing paths from passing dangerously close to obstacles. Through theoretical convergence analysis, Matrix Laboratory(MATLAB) simulations, and Robot Operating System(ROS) based physical experiments, the algorithm demonstrates significant improvements over traditional methods, reducing turning points by approximately 15%, decreasing planning time by nearly 50%, and substantially optimizing path length. Although the generated paths may not achieve optimality in every specific metric, the method maintains robust performance across environments with obstacle densities ranging from 10% to 35%, effectively enhancing operational efficiency, safety, and environmental adaptability for unmanned vehicle systems. These AI-powered enhancements not only improve the algorithm’s performance in simulation but also demonstrate practical applicability in real world scenarios such as logistics sorting, warehouse patrols, and indoor autonomous navigation, offering a reliable and efficient path planning solution for intelligent unmanned systems.

Graph pattern matching (GPM) aims to find subgraphs isomorphic to user-specified patterns within a large graph. Due to its ability to reveal potential relationships among entities in complex networks, it is widely applied in various fields, such as mining molecular structures in bioinformatics, detecting fraud in cloud-based e-commerce, and querying knowledge graphs in large language model. The explosion of data brought by the AI era has rendered traditional GPM systems inadequate for real-world needs. Due to the intricate data dependencies of GPM tasks, most SOTA GPM systems currently have limited scalability and performance, they perform well in small graph mining with single node but cannot scale to modern clusters with GPU acceleration. This paper introduces JUPITER, the first system capable of matching patterns on large graph across multi-node GPU clusters, which can handle graphs 10 times larger than SOTAs with the same memory resources. Its core principle is to delegate computation to the data-residing processing unit rather than pulling data to the computation location, which greatly improves communication efficiency. Experimental results show that JUPITER can reduce communication volume by two orders of magnitude compared to SOTA subgraph matching systems, achieving up to 120× speedup and an average of 21.5× speedup.

Graph pattern matching (GPM) algorithms search for specific topological patterns in large networks, revealing relationships between entities. They are applied in fields like social network analysis, cheminformatics, recommendation systems, classification systems, and anomaly detection. However, GPM is highly time-consuming, lacks polynomial-time algorithms, and is challenging to scale for distributed settings. These GPM algorithms require 2-hop neighbors and generate many intermediate results during execution. Traditional systems use a pull-based method, leading to significant memory and communication overhead, which complicates scaling to larger data sizes. This paper introduces a push-based method and further designs a push-pull hybrid algorithm. Based on the hybrid algorithms, we build a GPM system Pisces for efficiently matching patterns on partitioned graphs. With optimizations in the merge context, it achieves significant performance improvements over state-of-the-art GPM systems.

Graph Pattern Mining (GPM) has made significant progress in recent years, supporting a range of big data applications. However, GPM applications are memory-intensive as they require a tremendous amount of edge checking, which involves enumerating all possible vertex pairs and checking their connectivity or counting the common neighbors of each pair. Adjacent list or CSR format is widely used in recent GPM systems to overcome the sparsity of real-world graph, at the cost of sacrificing the ability to query the connectivity of two vertices in O(1) compared with the raw adjacent matrix. In this paper, we rethink the format to store graph in GPM systems, by partial "restoring" graph from CSR to adjacent matrix, we can significantly accelerate checking connectivity or counting common neighbors for GPM algorithms. However, directly storing graph in adjacent matrix is cost-prohibitive, thus we propose GLumin, by selectively constructing a lookup table (LUT) at the runtime, which holds the connectivity that can be repeatedly retrieved at low cost. The LUTs are compressed and tailored to fit the hierarchical memory architecture like shared memory of GPU or cache of CPU. We adopt GLumin technique to several SOTA GPM systems like AutoMine, G2Miner and GraphFold, bringing two orders of magnitude speedup to them on more than 100 different graphs and 24 patterns, demonstrating the general applicability and significance of our methods.

Graph pattern matching (GPM) is essential in fields like circuit logic synthesis, anomaly detection, social network analysis, cheminformatics, recommendation systems, and classification systems. Its NP-completeness and the irregular nature of graph data make scaling to distributed systems challenging, especially for complex supercomputers. Although utilizing architecture-specific optimization can improve the performance of Graph Pattern Matching on large-scale data, such ad-hoc solution lacks performance portability that not only causes vendor lock-in but also complicates the parallel evolution of GPM software with hardware architectures. This paper proposes Cosmos, a domain-specific software distributed shared memory model (DSM) that shields diversity of supercomputers from users and developers, achieving both performance portability and performance. This approach enables the same code scaling to thousands of nodes across different supercomputers while maintaining performance comparable to manually optimized versions.

Graph Neural Networks (GNNs) have garnered significant interest across various domains due to their efficacy in learning from graph-structured data. In pursuit of heightened performance, numerous GNN frameworks have emerged recently. However, recent work tends to study performance optimization at the computational graph level and operator level separately, and the existing optimization techniques rely on pattern matching and manual intervention, driven by human expertise. Consequently, their performances remain sub-optimal and sensitive to input graphs and GNN models. In this work, we develop an efficient coordinated strategy named AlphaGNN, which achieves an effective combination of computational graph optimization and operator optimization. To render this coordinated optimization impactful, a rule-based computational graph optimization and a performance-driven operator optimization are proposed. The experimental results confirm that AlphaGNN achieves up to 12.39 × (2.94 × on average) performance improvement over the state-of-the-art methods on diverse GNN models.

Graph Pattern Mining (GPM) applications are memory intensive as they require a tremendous amount of edge checks. In recent years, the "set-centric" abstraction has gained attention for its powerful expressive abilities. By leveraging relational algebra, they optimized algorithms with methods like matching orders, early termination, automorphism-breaking, and result reuse to reduce redundancy. However, these approaches primarily address coarse-grained redundancy from exactly the same set formulas, neglecting that the data graph's inherent locality may lead to fine-grained duplicated edge checks. In fact, even unrelated set operations may check the same pair of vertices. This paper introduces the set union operation to the set-centric abstraction to fuse duplicated edge checks into one. It maintains the expressive power of relational algebra and previous optimizations while effectively avoids fine-grained redundancy in GPM tasks. Compared to state-of-the-art methods, our method achieves significant speedup on a V100 GPU cluster, demonstrating up to 305 × faster performance than the state-of-the-art GPM system G2Miner.

Subgraph matching (SM) has witnessed tremendous progress in recent years, enabling a broad spectrum of big data applications. SM applications are extremely computeintensive since they require tremendous set operations, i.e., enumerating all the possible vertex pairs and counting the common neighbor of each pair. GPU is potentially promising hardware to accelerate SM applications due to its massive parallelism. However, SM applications achieve low efficiency and often fail to deliver high performance in multi-GPU systems owing to irregular edge distribution which exhausts the computing power and aggravates the load-imbalance problems. Although many existing frameworks have proffer numerous methods at high-level to improve the efficiency of GPU-based SM, e.g., assign matching order, early termination, and automorphismbreaking, the low-level issues on GPU architecture and system, e.g., thread mapping, graph partitions are not well addressed. In this work, we develop GraphPar, an efficient SM system targeting multi-GPUs. GraphPar proposes an effective workload- aware scheduling and an efficient set operation designing, which could successfully reduce the stragglers and significantly accelerate SM. Experiments on a V100 GPU cluster show that GraphPar is up to 4.21 × faster than the state-of-the-art GPU-based GPM system G2Miner.

With the rapid development of multi-sensor fusion technology in various industrial fields, many composite images closely related to human life have been produced. To meet the rapidly growing needs of various image-based applications, we have established the first multi-source composite image (MSCI) database for image quality assessment (IQA). Our MSCI database contains 80 reference images and 1600 distorted images, generated by four advanced compression standards with five distortion levels. In particular, these five distortion levels are determined based on the first five just noticeable difference (JND) levels. Moreover, we verify the IQA performance of some representative methods on our MSCI database. The experimental results show that the performance of the existing methods on the MSCI database needs to be further improved.