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GPF : a framework for general packet classification on GPU co-processors

机译:GPF:GPU协处理器上的通用数据包分类框架

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摘要

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.
机译:本文探讨了GPF的设计和实验实现,GPF是一种与协议无关的新型多匹配数据包分类框架。该框架的目标是针对CUDA API在NVIDIA GPU平台上灵活,高效地执行而进行优化的,但将来应该不难移植到其他平台(例如OpenCL).GPF的构思和开发是为了加快对GPF的分类。大型数据包捕获文件,例如Network Telescopes收集的文件。它使用多阶段SIMD分类过程,该过程利用数据包集的并行性和过滤器程序中的冗余性,以便以极高的速率针对多个过滤器对数据包捕获进行分类。最终的框架由分类,编译和缓冲组件组成,可有效利用GPU资源对任意协议进行分类,并为每个数据包返回多个过滤器结果。通过针对多个过滤器程序测试实验原型实现,对描述的分类功能进行了验证和评估,在来自三代GPU平台的设备上具有不同的复杂性。除了显着提高处理结果的速度外,分析还表明原型分类功能可预测地执行,并且相对于数据包计数和过滤器复杂度均呈线性增长。此外,分类吞吐量(packets / s)基本上保持不变,无论基础数据包数据如何,因此对特定过滤器进行分类时的有效数据速率受处理捕获中数据包平均大小的影响很大。在对所有IPv4数据包进行分类(大小从70字节到1KB)的情况下,GPU分类内核在GTX 275上观察到的数据速率范围从60Gbps到900Gbps,在GTX 480上从220Gbps到3.3Tbps。在针对IPv4和IPv6协议识别所有ARP,TCP,UDP和ICMP数据包的情况下,对于70B和1KB数据包,有效数据速率分别为15Gbps至220Gbps(GTX 275)和50Gbps至740Gbps(GTX 480)。

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    Nottingham Alastair;

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  • 年度 2012
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  • 原文格式 PDF
  • 正文语种 {"code":"en","name":"English","id":9}
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