GFlow: Towards GPU-based HighPerformance 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.
Introduction
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This paper investigates the acceleration of Software based
OpenFlow switches, equipped with commodity off-theshelf hardware, for high-performance table matching.
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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.
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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
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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
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Compared with Existing Approaches
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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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