9072

A Dynamic IP Lookup Architecture using Parallel Multiple Hash in GPU-based Software Router

Xin Yao, Yaping Lin, Gang Wang, Guoliang Hu
School of Information Science and Engineering, Hunan University, Changsha 410082, China
Journal of Computational Information Systems 9: 3, 967-976, 2013
@article{yao2013dynamic,

   title={A Dynamic IP Lookup Architecture using Parallel Multiple Hash in GPU-based Software Router},

   author={YAO, Xin and LIN, Yaping and WANG, Gang and HU, Guoliang},

   journal={Journal of Computational Information Systems},

   volume={9},

   number={3},

   pages={967–976},

   year={2013}

}

Download Download (PDF)   View View   Source Source   

419

views

As the fiber propagation velocity grows and the routing scale expands, IP lookup speed becomes the major bottleneck of high-performance network. Its efficiency directly determines the throughput of the entire routing channel. Recently, Graphics Processing Units (GPUs), highly parallel, flexibility for program and low price, is widely adopted in different areas including software router. In this paper, we propose an architecture named GPU-Based Parallel Multiple Hash IP Lookup/Update Architecture (GPMHIA) to perform high-performance IP address lookup/update. We design a high-speed IP address lookup/update architecture based on IPv4, and this architecture can be easily extended to IPv6. Meanwh-ile, parallel multiple hash functions have been used for accelerating the matching speed and we propose an Adaptive Optimal XOR Hash (AOXH) to construct hash functions. Simulation results using 5 real IPv4 forwarding tables present that GPMHIA is a high-performance IP forwarding architecture.
VN:F [1.9.22_1171]
Rating: 0.0/5 (0 votes cast)

* * *

* * *

Like us on Facebook

HGPU group

128 people like HGPU on Facebook

Follow us on Twitter

HGPU group

1194 peoples are following HGPU @twitter

Featured events

* * *

Free GPU computing nodes at hgpu.org

Registered users can now run their OpenCL application at hgpu.org. We provide 1 minute of computer time per each run on two nodes with two AMD and one nVidia graphics processing units, correspondingly. There are no restrictions on the number of starts.

The platforms are

Node 1
  • GPU device 0: AMD/ATI Radeon HD 5870 2GB, 850MHz
  • GPU device 1: AMD/ATI Radeon HD 6970 2GB, 880MHz
  • CPU: AMD Phenom II X6 @ 2.8GHz 1055T
  • RAM: 12GB
  • OS: OpenSUSE 13.1
  • SDK: AMD APP SDK 2.9
Node 2
  • GPU device 0: AMD/ATI Radeon HD 7970 3GB, 1000MHz
  • GPU device 1: nVidia GeForce GTX 560 Ti 2GB, 822MHz
  • CPU: Intel Core i7-2600 @ 3.4GHz
  • RAM: 16GB
  • OS: OpenSUSE 12.2
  • SDK: nVidia CUDA Toolkit 6.0.1, AMD APP SDK 2.9

Completed OpenCL project should be uploaded via User dashboard (see instructions and example there), compilation and execution terminal output logs will be provided to the user.

The information send to hgpu.org will be treated according to our Privacy Policy

HGPU group © 2010-2014 hgpu.org

All rights belong to the respective authors

Contact us: