- Title
- GPF : a framework for general packet classification on GPU co-processors
- Title
- GPU Packet Filter : framework for general packet classification on Graphics Processing Unit co-processors
- Creator
- Nottingham, Alastair
- Subject
- Graphics processing units
- Subject
- Coprocessors
- Subject
- Computer network protocols
- Subject
- Computer networks -- Security measures
- Subject
- NVIDIA Corporation
- Date Issued
- 2012
- Date
- 2012
- Type
- Thesis
- Type
- Masters
- Type
- MSc
- Identifier
- vital:4661
- Identifier
- http://hdl.handle.net/10962/d1006662
- Identifier
- Graphics processing units
- Identifier
- Coprocessors
- Identifier
- Computer network protocols
- Identifier
- Computer networks -- Security measures
- Identifier
- NVIDIA Corporation
- Description
- This thesis explores the design and experimental implementation of GPF, a novel protocol-independent, multi-match packet classification framework. This framework is targeted and optimised for flexible, efficient execution on NVIDIA GPU platforms through the CUDA API, but should not be difficult to port to other platforms, such as OpenCL, in the future. GPF was conceived and developed in order to accelerate classification of large packet capture files, such as those collected by Network Telescopes. It uses a multiphase SIMD classification process which exploits both the parallelism of packet sets and the redundancy in filter programs, in order to classify packet captures against multiple filters at extremely high rates. The resultant framework - comprised of classification, compilation and buffering components - efficiently leverages GPU resources to classify arbitrary protocols, and return multiple filter results for each packet. The classification functions described were verified and evaluated by testing an experimental prototype implementation against several filter programs, of varying complexity, on devices from three GPU platform generations. In addition to the significant speedup achieved in processing results, analysis indicates that the prototype classification functions perform predictably, and scale linearly with respect to both packet count and filter complexity. Furthermore, classification throughput (packets/s) remained essentially constant regardless of the underlying packet data, and thus the effective data rate when classifying a particular filter was heavily influenced by the average size of packets in the processed capture. For example: in the trivial case of classifying all IPv4 packets ranging in size from 70 bytes to 1KB, the observed data rate achieved by the GPU classification kernels ranged from 60Gbps to 900Gbps on a GTX 275, and from 220Gbps to 3.3Tbps on a GTX 480. In the less trivial case of identifying all ARP, TCP, UDP and ICMP packets for both IPv4 and IPv6 protocols, the effective data rates ranged from 15Gbps to 220Gbps (GTX 275), and from 50Gbps to 740Gbps (GTX 480), for 70B and 1KB packets respectively.
- Description
- LaTeX with hyperref package
- Format
- 193 leaves
- Format
- Publisher
- Rhodes University
- Publisher
- Faculty of Science, Computer Science
- Language
- English
- Rights
- Nottingham, Alastair
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