A high-level architecture for efficient packet trace analysis on gpu co-processors
- Authors: Nottingham, Alastair , Irwin, Barry V W
- Date: 2013
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/429572 , vital:72623 , 10.1109/ISSA.2013.6641052
- Description: This paper proposes a high-level architecture to support efficient, massively parallel packet classification, filtering and analysis using commodity Graphics Processing Unit (GPU) hardware. The proposed architecture aims to provide a flexible and efficient parallel packet processing and analysis framework, supporting complex programmable filtering, data mining operations, statistical analysis functions and traffic visualisation, with minimal CPU overhead. In particular, this framework aims to provide a robust set of high-speed analysis functionality, in order to dramatically reduce the time required to process and analyse extremely large network traces. This architecture derives from initial research, which has shown GPU co-processors to be effective in accelerating packet classification to up to tera-bit speeds with minimal CPU overhead, far exceeding the bandwidth capacity between standard long term storage and the GPU device. This paper provides a high-level overview of the proposed architecture and its primary components, motivated by the results of prior research in the field.
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- Date Issued: 2013
Towards a GPU accelerated virtual machine for massively parallel packet classification and filtering
- Authors: Nottingham, Alastair , Irwin, Barry V W
- Date: 2013
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/430295 , vital:72681 , https://doi.org/10.1145/2513456.2513504
- Description: This paper considers the application of GPU co-processors to accelerate the analysis of packet data, particularly within extremely large packet traces spanning months or years of traffic. Discussion focuses on the construction, performance and limitations of the experimental GPF (GPU Packet Filter), which employs a prototype massively-parallel protocol-independent multi-match algorithm to rapidly compare packets against multiple arbitrary filters. The paper concludes with a consideration of mechanisms to expand the flexibility and power of the GPF algorithm to construct a fully programmable GPU packet classification virtual machine, which can perform massively parallel classification, data-mining and data-transformation to explore and analyse packet traces. This virtual machine is a component of a larger framework of capture analysis tools which together provide capture indexing, manipulation, filtering and visualisation functions.
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- Date Issued: 2013