Ziming Wang;Kai Zhang;Yangming Lv;Yinglong Wang;Zhigang Zhao;Zhenying He;Yinan Jing;X. Sean Wang
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RTOD: Efficient Outlier Detection With Ray Tracing Cores
Outlier detection in data streams is a critical component in numerous applications, such as network intrusion detection, financial fraud detection, and public health. To detect abnormal behaviors in real-time, these applications generally have stringent requirements for the performance of outlier detection. This paper proposes RTOD, a high-performance outlier detection approach that utilizes RT cores in modern GPUs for acceleration. RTOD transforms distance-based outlier detection in data streams into an efficient ray tracing job. By creating spheres centered at points within a window and casting rays from each point, RTOD identifies the outlier points according to the number of intersections between rays and spheres. Besides, we propose two optimization techniques, namely Grid Filtering and Ray-BVH Inversion, to further accelerate the detection efficiency of RT cores. Experimental results show that RTOD achieves up to 9.9× speedups over existing start-of-the-art outlier detection algorithms.
期刊介绍:
The IEEE Transactions on Knowledge and Data Engineering encompasses knowledge and data engineering aspects within computer science, artificial intelligence, electrical engineering, computer engineering, and related fields. It provides an interdisciplinary platform for disseminating new developments in knowledge and data engineering and explores the practicality of these concepts in both hardware and software. Specific areas covered include knowledge-based and expert systems, AI techniques for knowledge and data management, tools, and methodologies, distributed processing, real-time systems, architectures, data management practices, database design, query languages, security, fault tolerance, statistical databases, algorithms, performance evaluation, and applications.