Breadth-first search(BFS) is an important kernel for graph traversal and has been used by many graph processing applications. Extensive studies have been devoted in boosting the performance of BFS. As the most effective solution, GPU-acceleration achieves the state-of-the-art result of 3.3×109 traversed edges per second on a NVIDIA Tesla C2050 GPU. A novel vertex frontier based GPU BFS algorithm is proposed, and its main features are three-fold. Firstly, to obtain a better workload balance for irregular graphs, a virtual-queue task decomposition and mapping strategy is introduced for vertex frontier expanding. Secondly, a global deduplicate detection scheme is proposed to remove reduplicative vertices from vertex frontier effectively. Finally, a GPU-based bottom-up BFS approach is employed to process large frontier. The experimental results demonstrate that the algorithm can achieve 10% improvement over the state-of-the-art method on diverse graphs. Especially, it exhibits 2-3 times speedup on low-diameter and scale-free graphs over the state-of-the-art on a NVIDIA Tesla K20 c GPU, reaching a peak traversal rate of 11.2×109 edges/s.
Feature-based image matching algorithms play an indispensable role in automatic target recognition(ATR).In this work,a fast image matching algorithm(FIMA)is proposed which utilizes the geometry feature of extended centroid(EC)to build affine invariants.Based on affine invariants of the length ratio of two parallel line segments,FIMA overcomes the invalidation problem of the state-of-the-art algorithms based on affine geometry features,and increases the feature diversity of different targets,thus reducing misjudgment rate during recognizing targets.However,it is found that FIMA suffers from the parallelogram contour problem and the coincidence invalidation.An advanced FIMA is designed to cope with these problems.Experiments prove that the proposed algorithms have better robustness for Gaussian noise,gray-scale change,contrast change,illumination and small three-dimensional rotation.Compared with the latest fast image matching algorithms based on geometry features,FIMA reaches the speedup of approximate 1.75 times.Thus,FIMA would be more suitable for actual ATR applications.
A novel framework for parallel subgraph isomorphism on GPUs is proposed, named GPUSI, which consists of GPU region exploration and GPU subgraph matching. The GPUSI iteratively enumerates subgraph instances and solves the subgraph isomorphism in a divide-and-conquer fashion. The framework completely relies on the graph traversal, and avoids the explicit join operation. Moreover, in order to improve its performance, a task-queue based method and the virtual-CSR graph structure are used to balance the workload among warps, and warp-centric programming model is used to balance the workload among threads in a warp. The prototype of GPUSI is implemented, and comprehensive experiments of various graph isomorphism operations are carried on diverse large graphs. The experiments clearly demonstrate that GPUSI has good scalability and can achieve speed-up of 1.4–2.6 compared to the state-of-the-art solutions.
