Multi-Terabit IP Lookup Using Parallel Bidirectional Pipelines Author: Weirong Jiang, Viktor K. Prasanna Publisher: May 2008 CF '08: Proceedings of the 2008 conference on Computing frontiers ACM Presenter: Yu-Ping Chiang Date: 2008/09/16

Outline Overview  Front End  Back End Memory Balancing  Trie Partitioning  Subtrie-to-Pipeline Mapping  Node-to-Stage Mapping Performance

Front End Receive packets Dispatch packets to pipelines:  cache hit / miss  set delay

Back End Process packets Output retrieved next-hop information:  using delay to retrieve output information

Outline Overview  Front End  Back End Memory Balancing  Trie Partitioning  Subtrie-to-Pipeline Mapping  Node-to-Stage Mapping Performance

Trie Partitioning Initial stride (I)  following section: I=12 I=2

Subtrie-to-Pipeline Mapping Problem formulation Algorithm – O(KP) K = # of tries P = # of pipelines

Performance

Node-to-Stage Mapping Problem formulation Constraint: ancestor mapped preceding to child. Main idea:  two subtries mapped onto different direction.  two same trie level nodes mapped onto different stages.

Inversion:  Methods: largest leaf least height largest leaf per height least average depth per leaf (use in following section)  Inversion Factor (IFR) (in following section: 4~8)

O(HN) Node fields:  Distance to child  Memory address of child H = # of pipeline stages N = total # of trie nodes

Outline Overview  Front End  Back End Memory Balancing  Trie Partitioning  Subtrie-to-Pipeline Mapping  Node-to-Stage Mapping Performance

Memory: 1.8 MB  (13+5)*2^13*25*4=14.75Mb=1.8MB  18 KB/stage G packets / sec  7.5 PPC*2.5GHz=18.75G packets/sec Power consumption: 0.2 W / IP lookup  0.008*25=0.2W