GPU Accelerated Anomaly Detection of Large Scale Light Curves
Document Type
Conference Proceeding
Publication Date
9-22-2020
Abstract
Identifying anomalies in millions of stars in real time is a great challenge. In this paper, we develop a matched filtering based algorithm to detect a typical anomaly, microlensing. The algorithm can detect short timescale microlensing events with high accuracy at their early stage with a very low false-positive rate. Furthermore, a GPU accelerated scalable computational framework, which can enable real time follow-up observation, is designed. This framework efficiently divides the algorithm between CPU and GPU, accelerating large scale light curve processing to meet low latency requirements. Experimental results show that the proposed method can process 200,000 stars (the maximum number of stars processed by a single GWAC telescope) in approximately 3.34 seconds with current commodity hardware while achieving an accuracy of 92% and an average detection occurring approximately 14% before the peak of the anomaly with zero false alarm. Working together with the proposed sharding mechanism, the framework is positioned to be extendable to multiple GPUs to improve the performance further for the higher data throughput requirements of next-generation telescopes.
Identifier
85099367611 (Scopus)
ISBN
[9781728192192]
Publication Title
2020 IEEE High Performance Extreme Computing Conference Hpec 2020
External Full Text Location
https://doi.org/10.1109/HPEC43674.2020.9286242
Grant
2016YFB1000602
Fund Ref
National Key Research and Development Program of China
Recommended Citation
Minor, Austin Chase; Du, Zhihui; Sun, Yankui; Bader, David A.; Wu, Chao; and Wei, Jianyan, "GPU Accelerated Anomaly Detection of Large Scale Light Curves" (2020). Faculty Publications. 5004.
https://digitalcommons.njit.edu/fac_pubs/5004