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Streaming data correlation on GPUs

Fotopoulos Spyridon, Malakonakis Pavlos, Chrysos Grigorios, Dollas Apostolos

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Extent6 pagesen
TitleStreaming data correlation on GPUsen
CreatorFotopoulos Spyridonen
CreatorΦωτοπουλος Σπυριδωνel
CreatorMalakonakis Pavlosen
CreatorΜαλακωνακης Παυλοςel
CreatorChrysos Grigoriosen
CreatorΧρυσος Γρηγοριοςel
CreatorDollas Apostolosen
CreatorΔολλας Αποστολοςel
PublisherInstitute of Electrical and Electronics Engineersen
Content SummaryDistributed systems have been widely used for applications that need real-time processing over high volume and high speed data streams. This work presents the architecture and the implementation of a correlation algorithm for streaming data on a Graphic Processing Unit (GPU). The proposed system accelerates the correlation calculation of the Hayashi-Yoshida algorithm up to 10x vs. a conventional distributed system, and these performance characteristics apply to a broad category of correlation estimators. Furthermore, our system offers real-time correlation computation over high-speed streaming data and demonstrates how a batch processing algorithm can be applied to real-time streaming data. The results show that GPUs are a highly promising platform for correlation estimators as they improve significantly the volume of streaming data that can be processed in real time vs. other approaches that use 'unlimited' conventional computing resources.en
Type of ItemΠλήρης Δημοσίευση σε Συνέδριοel
Type of ItemConference Full Paperen
Date of Item2019-09-06-
Date of Publication2018-
SubjectReal time processingen
SubjectStream processingen
Bibliographic CitationS. Fotopoulos, P. Malakonakis, G. Chrysos and A. Dollas, "Streaming data correlation on GPUs," in 7th International Conference on Modern Circuits and Systems Technologies, 2018, pp. 1-5. doi: 10.1109/MOCAST.2018.8376588en