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Latency-Aware Packet Processing on CPU-GPU Heterogeneous Systems
Linköping University, Department of Computer and Information Science, Software and Systems. Linköping University, Faculty of Science & Engineering.
Linköping University, Department of Computer and Information Science, Software and Systems. Linköping University, Faculty of Science & Engineering.
Linköping University, Department of Computer and Information Science, Software and Systems. Linköping University, Faculty of Science & Engineering. Ericsson Sweden.
Ericsson Sweden.
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2017 (English)In: DAC '17 Proceedings of the 54th Annual Design Automation Conference 2017, New York, NY, USA: Association for Computing Machinery (ACM), 2017Conference paper, Published paper (Refereed)
Abstract [en]

In response to the tremendous growth of the Internet, towards what we call the Internet of Things (IoT), there is a need to move from costly, high-time-to-market specific-purpose hardware to flexible, low-time-to-market general-purpose devices for packet processing. Among several such devices, GPUs have attracted attention in the past, mainly because the high computing demand of packet processing applications can, potentially, be satisfied by these throughput-oriented machines. However, another important aspect of such applications is the packet latency which, if not handled carefully, will overshadow the throughput benefits. Unfortunately, until now, this aspect has been mostly ignored. To address this issue, we propose a method that considers the variable bit rate of the traffic and, depending on the current rate, minimizes the latency, while meeting the rate demand. We propose a persistent kernel based software architecture to overcome the challenges inherent in GPU implementation like kernel invocation overhead, CPU-GPU communication and memory access overhead. We have chosen packet classification as the packet processing application to demonstrate our technique. Using the proposed approach, we are able to reduce the packet latency on average by a factor of 3.5, compared to the state-of-the-art solutions, without any packet drop.

Place, publisher, year, edition, pages
New York, NY, USA: Association for Computing Machinery (ACM), 2017.
National Category
Computer Science
Identifiers
URN: urn:nbn:se:liu:diva-141212DOI: 10.1145/3061639.3062269Scopus ID: 2-s2.0-85023612665ISBN: 978-1-4503-4927-7 (print)OAI: oai:DiVA.org:liu-141212DiVA: diva2:1144800
Conference
Design Automation Conference, Austin, TX, USA, June 18-22, 2017
Available from: 2017-09-27 Created: 2017-09-27 Last updated: 2017-10-13Bibliographically approved

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Maghazeh, ArianBordoloi, Unmesh D.Dastgeer, Usman

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Maghazeh, ArianBordoloi, Unmesh D.Dastgeer, UsmanEles, PetruPeng, Zebo
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Citation style
  • apa
  • harvard1
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Language
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  • sv-SE
  • Other locale
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Output format
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