Automatically partitioning packet processing applications for pipelined architectures

Author:

Dai Jinquan1,Huang Bo1,Li Long1,Harrison Luddy2

Affiliation:

1. Intel China Software Center, Shanghai, PRC

2. Univ. of Illinois at Urbana-Champaign, Urbana, IL

Abstract

Modern network processors employs parallel processing engines (PEs) to keep up with explosive internet packet processing demands. Most network processors further allow processing engines to be organized in a pipelined fashion to enable higher processing throughput and flexibility. In this paper, we present a novel program transformation technique to exploit parallel and pipelined computing power of modern network processors. Our proposed method automatically partitions a sequential packet processing application into coordinated pipelined parallel subtasks which can be naturally mapped to contemporary high-performance network processors. Our transformation technique ensures that packet processing tasks are balanced among pipeline stages and that data transmission between pipeline stages is minimized. We have implemented the proposed transformation method in an auto-partitioning C compiler product for Intel Network Processors. Experimental results show that our method provides impressive speed up for the commonly used NPF IPv4 forwarding and IP forwarding benchmarks. For a 9-stage pipeline, our auto-partitioning C compiler obtained more than 4X speedup for the IPv4 forwarding PPS and the IP forwarding PPS (for both the IPv4 traffic and IPv6 traffic).

Publisher

Association for Computing Machinery (ACM)

Subject

Computer Graphics and Computer-Aided Design,Software

Reference30 articles.

1. Challenges in Building Network Processor Based Solutions http://www.futsoft.com/pdf/NPwp.pdf Challenges in Building Network Processor Based Solutions http://www.futsoft.com/pdf/NPwp.pdf

2. Intel IXP family of Network Processors www.intel.com/design/network/products/npfamily/index.htm Intel IXP family of Network Processors www.intel.com/design/network/products/npfamily/index.htm

3. IBM PowerNP Network Processors http://www-3.ibm.com/chips/techlib/techlib.nsf/products/IBM_PowerNP_NP4GS3 IBM PowerNP Network Processors http://www-3.ibm.com/chips/techlib/techlib.nsf/products/IBM_PowerNP_NP4GS3

4. CPort Network Processor family http://www.windriver.com/cgi-bin/partnerships/directory/viewProd.cgi?id=1371 CPort Network Processor family http://www.windriver.com/cgi-bin/partnerships/directory/viewProd.cgi?id=1371

5. Agere's PayloadPlus Family of Network Processors http://www.agere.com/telecom/network_processors.html Agere's PayloadPlus Family of Network Processors http://www.agere.com/telecom/network_processors.html

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