GFlow: Towards GPU-based High-Performance Table Matching in

OpenFlow Switches

Author : Kun Qiu, Zhe Chen, Yang Chen, Jin Zhao, Xin WangPublisher : Information Networking (ICOIN), 2015 International Conference on Presenter: Tung-yin ChiDate: 2015/3/25

Department of Computer Science and Information Engineering National Cheng Kung University, Taiwan R.O.C.

Introduction

This paper investigates the acceleration of Software based OpenFlow switches, equipped with commodity off-the-shelf hardware, for high-performance table matching.

In our work, we leverage the power of GPUs to accelerate table matching in software-based OpenFlow switches. We propose GFlow, which can handle OpenFlow table matching in a parallel fashion.

Based on our extensive evaluations, we can see the GFlow is 8 to 10 times faster than existing GPU-based matching algorithm.

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Data Structure: ItemGraph

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ItemGraph

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ItemGraph

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ItemGraph

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ItemGraph

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Matching on the ItemGraph

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The architecture of GFlow

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Parallel matching processing the GPU

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Experimental Environment

commodity PC CPU : Intel I7 2600K @3.2GHz

• 4 cores/8 threads Memory : 8GB DDR3-1333 GPU : Nvidia GTX 470 @607MHz

• 448 cores, 1280MB GDDR5 memory@3384MHz OS : Fedora 18 (Kernel version 3.8.11-200) The algorithm in GPU is implemented by

OpenCLNational Cheng Kung University CSIE Computer & Internet Architecture Lab

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Compared with Existing Approaches

Linear search used by OpenFlow vSwitch Linear search with CPU parallel optimization LightFlow, the GPU parallel optimization GFlow, the work in this paper

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Experimental Result

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Experimental Result

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Experimental Result

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