论文标题
在非易失性记忆上的高效且磨损水平的频繁模式开采
An Efficient and Wear-Leveling-Aware Frequent-Pattern Mining on Non-Volatile Memory
论文作者
论文摘要
常见的模式采矿是一种揭示数据背后有价值的隐藏趋势的常见方法。但是，现有的频繁模式采矿算法是为DRAM设计的，而不是持续的记忆（PMS），由于DRAM和PMS在PMS上运行时的特性完全不同，这可能导致严重的性能和能量开销。在本文中，我们提出了一种有效且磨损水平的频繁模式挖掘方案WFPM，以解决此问题。提出的WFPM通过一系列实验进行评估，该实验基于从多元化的应用程序场景中实现的数据集进行评估，其中WFPM在EVFP-Tree上实现了32.0％的绩效提高，并将标头表的NVM寿命延长了7.4倍。
Frequent-pattern mining is a common approach to reveal the valuable hidden trends behind data. However, existing frequent-pattern mining algorithms are designed for DRAM, instead of persistent memories (PMs), which can lead to severe performance and energy overhead due to the utterly different characteristics between DRAM and PMs when they are running on PMs. In this paper, we propose an efficient and Wear-leveling-aware Frequent-Pattern Mining scheme, WFPM, to solve this problem. The proposed WFPM is evaluated by a series of experiments based on realistic datasets from diversified application scenarios, where WFPM achieves 32.0% performance improvement and prolongs the NVM lifetime of header table by 7.4x over the EvFP-Tree.
