利用ADIOS-P挖掘隐藏混合上下文,提高预测预取精度

J. Choi, H. Abbasi, D. Pugmire, N. Podhorszki, S. Klasky, Cristian Capdevila, M. Parashar, M. Wolf, J. Qiu, G. Fox
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引用次数: 3

摘要

预测性预取(Predictive pre-fetcher)是一种预测未来的数据访问事件并在用户请求之前加载数据的方法,在文件系统或web内容服务器中得到了广泛的研究,以减少数据加载延迟。特别是在科学数据可视化中,预取可以减少IO等待时间。为了提高准确率,我们采用了数据挖掘技术来提取隐藏信息。更具体地说,我们应用数据挖掘技术来发现数据访问模式中隐藏的上下文,并根据推断的上下文进行预测,以提高预测的准确性。特别是,我们执行了概率潜在语义分析(PLSA),这是一种在文本挖掘领域流行的基于混合模型的算法,从收集的用户访问模式中挖掘隐藏上下文,然后,我们在发现的上下文中运行预测器。我们利用确定性退火(DA)方法克服了局部最优问题,进一步改进了PLSA。在本文中,我们演示了如何应用PLSA和DA优化来挖掘用户数据访问模式中的隐藏上下文,并提高预测预取器的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Mining hidden mixture context with ADIOS-P to improve predictive pre-fetcher accuracy
Predictive pre-fetcher, which predicts future data access events and loads the data before users requests, has been widely studied, especially in file systems or web contents servers, to reduce data load latency. Especially in scientific data visualization, pre-fetching can reduce the IO waiting time. In order to increase the accuracy, we apply a data mining technique to extract hidden information. More specifically, we apply a data mining technique for discovering the hidden contexts in data access patterns and make prediction based on the inferred context to boost the accuracy. In particular, we performed Probabilistic Latent Semantic Analysis (PLSA), a mixture model based algorithm popular in the text mining area, to mine hidden contexts from the collected user access patterns and, then, we run a predictor within the discovered context. We further improve PLSA by applying the Deterministic Annealing (DA) method to overcome the local optimum problem. In this paper we demonstrate how we can apply PLSA and DA optimization to mine hidden contexts from users data access patterns and improve predictive pre-fetcher performance.
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