GPU Accelerated Anomaly Detection of Large Scale Light Curves

Austin Chase Minor, Zhihui Du, Yankui Sun, David A. Bader, Chao Wu, Jianyan Wei

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

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.

Original languageEnglish (US)
Title of host publication2020 IEEE High Performance Extreme Computing Conference, HPEC 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728192192
DOIs
StatePublished - Sep 22 2020
Event2020 IEEE High Performance Extreme Computing Conference, HPEC 2020 - Virtual, Waltham, United States
Duration: Sep 21 2020Sep 25 2020

Publication series

Name2020 IEEE High Performance Extreme Computing Conference, HPEC 2020

Conference

Conference2020 IEEE High Performance Extreme Computing Conference, HPEC 2020
Country/TerritoryUnited States
CityVirtual, Waltham
Period9/21/209/25/20

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computational Theory and Mathematics
  • Computer Networks and Communications
  • Computer Science Applications
  • Hardware and Architecture

Keywords

  • anomaly detection
  • GPU
  • matched filtering
  • performance optimization

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