GPU task parallelism for scalable anomaly detection

Stream computing has emerged as a new processing paradigm that processes incoming data streams from large numbers of sensors in real time. At the same time, many recent efforts have shown the suitability of GPGPU s for batch-typed long-running applications. However, few studies conduct the applicability of GPGPU to stream computing and also our first experiment shows that the performance does not scale as expected if one introduces the GPGPU to stream computing applications. This paper presents the workload characterization of GPGPU-based stream computing. We especially focus on computing SVD (Singular Value Decomposition) with GPGPUs for small-sized matrix since it can be widely applicable to real-time stream-based data mining applications such as real-time anomaly detection. In this paper, we not only show the workload characterization of GPGPU-based stream computing but also propose the optimization approach of SVD for stream computing applications called “GPU task Parallelism” to leverage the sufficient capability of GPGPUs. This optimization offers new levels of scalability for real-time operations with large numbers of sensors. Our experimental results show that GPU task parallelism provides roughly four-fold performance gains against a quad-core CPU for matrixes of 300 × 300 to 500 × 500 data values. We also implemented a stream-based change-point and anomaly detection system based on the optimized SVD and stream computing system called System S. By porting the optimized version of SVD to the distributed stream computing system, it was easy to exploit multiple GPUs on multiple nodes. The performance results showed performance around 7.6 times faster than CPUs. The scalability of our proposed system was tested up to 1,525 sensors, which were simultaneously handled for change-point detection every 5 seconds.