Distributed Shared Memory systems provide the abstraction of a shared address space among computing hosts interconnected via a private network, in a convenient and easy way to achieve high performance. However, there are several drawbacks to these systems, such as communication overhead, network latency, false sharing, coherence and page faults. By prefetching, one can overlap communication and computation though the Accumulated Waiting Phenomenon and the Waiting Synchronization Phenomenon affect overall performance. In this paper, an Effective Prefetch Strategy (EPS) is proposed, to improve the shortcomings of previous prefetching approaches and increases the prefetch page hit rate. In addition, our EPS strategy reduces the waiting time for each computing host upon barrier synchronizations and misprefetches. Experimental comparisons show that our proposed EPS strategy offers the best performance among existing prefetching strategies.
With the rapid development of network hardware technologies and network bandwidth, the high link speeds and huge amount of threats poses challenges to network intrusion detection systems, which must handle the higher network traffic and perform more complicated packet processing. In general, pattern matching is a highly computationally intensive process part of network intrusion detection systems. In this paper, we present an efficient GPU-based pattern matching algorithm by leveraging the computational power of GPUs to accelerate the pattern matching operations to increase the over-all processing throughput. From the experiment results, the proposed algorithm achieved a maximum traffic processing throughput of 2.4 Gbit/s. The results demonstrate that GPUs can be used effectively to speed up intrusion detection systems.
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