1 / 23

GAMT: A Fast and Scalable IP Lookup Engine for GPU-based Software Routers

GAMT: A Fast and Scalable IP Lookup Engine for GPU-based Software Routers. Author : Yanbiao Li, Dafang Zhang, Alex X. Liu and Jintao Zheng Publisher : ANCS 2013 Presenter: Yu Hao , Tseng Date: 2013/11/13. Outline. Introduction Background and Related Work

oke
Download Presentation

GAMT: A Fast and Scalable IP Lookup Engine for GPU-based Software Routers

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. GAMT: A Fast and Scalable IP Lookup Engine forGPU-based Software Routers Author: YanbiaoLi, DafangZhang, Alex X. Liu and Jintao Zheng Publisher: ANCS 2013 Presenter: Yu Hao, Tseng Date: 2013/11/13

  2. Outline • Introduction • Background and Related Work • GPU-Accelerated Lookup Engine • GPU-accelerated Multi-bit Trie • Performance Optimization • Experimental Evaluation

  3. Introduction • The GPU is becoming an emerging platform for high performance general-purpose computing. • J. Zhao et al. [27] - GPU-Accelerated Lookup Engine (GALE) • only applicable for IPv4 • decline sharply with the increase of update frequency • GPU-Accelerated Multi-bit Trie (GAMT) • Scale to IPv6 smoothly • Keep stable lookup throughput under highly frequent updates • Improve lookup performance with latency controlled

  4. Background and Related Work • Multi-bit Trie

  5. Background and Related Work (Cont.) • CUDA Programming Model • Coalescence of Global Memory Accesses • Overlapping Behaviors on the GPU • GPU-Accelerated IP Lookup Engine

  6. GPU-Accelerated Lookup Engine

  7. GPU-accelerated Multi-bit Trie • Encoding Rules and Lookup Approach

  8. GPU-accelerated Multi-bit Trie (Cont.) • Encoding Rules and Lookup Approach • Ex : 10001* • Step 1. Default (inf, jump) = (0, 1) • Step 2. (inf, jump) = (0 + 1, 1) = (1, 1) => (inf, jump) = (0, 2) • Step 3. (inf, jump) = (0 + 0, 2) = (0, 2) => (inf, jump) = (0, 3) • Step 4. (inf, jump) = (0 + 1, 3) = (1, 3) => (inf, jump) = (5, 0)

  9. GPU-accelerated Multi-bit Trie (Cont.) • Encoding Rules and Lookup Approach

  10. GPU-accelerated Multi-bit Trie (Cont.) • Update Mechanism

  11. GPU-accelerated Multi-bit Trie (Cont.) • Update Mechanism

  12. GPU-accelerated Multi-bit Trie (Cont.) • Architecture Overview

  13. Performance Optimization • Possibility of being Faster than GALE

  14. Performance Optimization (Cont.) • Optimized Multi-bit Trie

  15. Performance Optimization (Cont.) • Optimized Multi-bit Trie

  16. Performance Optimization (Cont.) • Delete in Lazy Mode • Multi-Stream Pipeline

  17. Experimental Evaluation • Evaluation Methodology

  18. Experimental Evaluation (Cont.) • Evaluation Methodology

  19. Experimental Evaluation (Cont.) • Lookup Performance

  20. Experimental Evaluation (Cont.) • Lookup Performance

  21. Experimental Evaluation (Cont.) • Lookup Performance

  22. Experimental Evaluation (Cont.) • Update Overhead

  23. Experimental Evaluation (Cont.) • Comprehensive Performance

More Related