Parallel Optimization of Frequent Algorithm on Multi-core Processors

Yu Zhang, Jianzhong Zhang, Jingdong Xu, Ying Wu
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Abstract

In this paper, we present a novel precision integrated framework (PRIF) to parallelize the famous Frequent algorithm in the context of multi-core processors. PRIF does this by equally distributing the stream of items into several sub-threads, each of which runs an optimized weighted Frequent algorithm independently and in parallel. The items with frequency increments over a pre-defined threshold in the sub-threads are sent to a merging thread which uses the same optimized weighted Frequent algorithm to provide the final e-deficient frequent items. The theoretical correctness analysis is presented. Experiments with three real traffic traces show that PRIF exhibits excellent scalability and delivers almost linear speedup.
频繁算法在多核处理器上的并行优化
在本文中,我们提出了一种新的精确集成框架(PRIF)来并行化多核处理器环境下著名的频繁算法。PRIF通过将项目流平均分配到几个子线程中来实现这一点,每个子线程独立并行地运行优化的加权频繁算法。子线程中频率增量超过预定义阈值的项被发送到合并线程,该线程使用相同的优化加权频繁算法来提供最终的e-deficient频繁项。给出了理论正确性分析。三个真实流量轨迹的实验表明,PRIF具有出色的可扩展性,并提供了几乎线性的加速。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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