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GFlow: Towards GPU-based High- Performance Table Matching in OpenFlow Switches Author : Kun Qiu, Zhe Chen, Yang Chen, Jin Zhao, Xin Wang Publisher : Information.

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Presentation on theme: "GFlow: Towards GPU-based High- Performance Table Matching in OpenFlow Switches Author : Kun Qiu, Zhe Chen, Yang Chen, Jin Zhao, Xin Wang Publisher : Information."— Presentation transcript:

1 GFlow: Towards GPU-based High- Performance Table Matching in OpenFlow Switches Author : Kun Qiu, Zhe Chen, Yang Chen, Jin Zhao, Xin Wang Publisher : Information Networking (ICOIN), 2015 International Conference on Presenter: Tung-yin Chi Date: 2015/3/25 Department of Computer Science and Information Engineering National Cheng Kung University, Taiwan R.O.C.

2 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. National Cheng Kung University CSIE Computer & Internet Architecture Lab 2

3 Data Structure: ItemGraph National Cheng Kung University CSIE Computer & Internet Architecture Lab 3

4 ItemGraph National Cheng Kung University CSIE Computer & Internet Architecture Lab 4

5 ItemGraph National Cheng Kung University CSIE Computer & Internet Architecture Lab 5 1 4 23

6 ItemGraph National Cheng Kung University CSIE Computer & Internet Architecture Lab 6 1 4 23

7 ItemGraph National Cheng Kung University CSIE Computer & Internet Architecture Lab 7 1 4 23

8 Matching on the ItemGraph National Cheng Kung University CSIE Computer & Internet Architecture Lab 8

9 The architecture of GFlow National Cheng Kung University CSIE Computer & Internet Architecture Lab 9

10 Parallel matching processing the GPU National Cheng Kung University CSIE Computer & Internet Architecture Lab 10

11 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 OpenCL National Cheng Kung University CSIE Computer & Internet Architecture Lab 11

12 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 National Cheng Kung University CSIE Computer & Internet Architecture Lab 12

13 Experimental Result National Cheng Kung University CSIE Computer & Internet Architecture Lab 13

14 Experimental Result National Cheng Kung University CSIE Computer & Internet Architecture Lab 14

15 Experimental Result National Cheng Kung University CSIE Computer & Internet Architecture Lab 15


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