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5 Conclusions

Network performance is increasingly important in all types of modern computer systems. Furthermore, architectural trends are pushing future microprocessors away from uniprocessor designs and toward architectures that incorporate multiple processing cores and/or thread contexts per chip. This trend necessitates the parallelization of the operating system's network stack. This paper evaluates message-based and connection-based parallelism within the network stack of a modern operating system. Further results and analysis are available in a technical report [17].

The uniprocessor version of the FreeBSD operating system performs quite well, but its performance degrades as additional connections are added. Though the MsgP, ConnP-T, and ConnP-L parallel network stacks can outperform the uniprocessor when using 4 cores, none of these organizations approach perfect speedup. This is caused by the higher locking overheads, poor cache efficiency, and high scheduling overhead of the parallel organizations. While MsgP can outperform a uniprocessor by 31% on average and by 62% for the heaviest connection loads, the enormous locking overhead incurred by such an approach limits its performance and prevents it from saturating available network resources. In contrast, ConnP-T eliminates intrastack locking completely by using thread serialization but incurs significant scheduling overhead that limits its performance to less than that of the uniprocessor kernel for all but the heaviest connection loads. ConnP-L mitigates the locking overhead of MsgP, by grouping connections to reduce global locking, and the scheduling overhead of ConnP-T, by using the requesting thread for network processing rather than invoking a network protocol thread. This results in good performance across a wide range of connections, delivering 5440 Mb/s for moderate connection loads and achieving a 126% improvement over the uniprocessor kernel when handling large connection loads.


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Next: Acknowledgments Up: An Evaluation of Network Previous: 4.3 Scheduler Overhead