NDLI: Improving the Effectiveness of Context-Based Prefetching with Multi-order Analysis

Content Provider	IEEE Xplore Digital Library
Author	Yong Chen Huaiyu Zhu Hui Jin Xian-He Sun
Copyright Year	2010
Abstract	Data prefetching is an effective way to accelerate data access in high-end computing systems and to bridge the increasing performance gap between processor and memory. In recent years, the contextbased data prefetching has received intensive attention because of its general applicability. In this study, we provide a preliminary analysis of the impact of orders on the effectiveness of the context-based prefetching. Motivated by the observations from the analytical results, we propose a new context-based prefetching method named Multi-Order Context-based (MOC) prefetching to adopt multi-order context analysis to increase the context-based prefetching effectiveness. We have carried out simulation testing with the SPECCPU2006 benchmarks via an enhanced CMP$im simulator. The simulation results show that the proposed MOC prefetching method outperforms the existing single-order prefetching and reduces the data-access latency effectively.
Starting Page	428
Ending Page	435
File Size	570240
Page Count	8
File Format	PDF
ISBN	9781424479184
ISSN	15302016
DOI	10.1109/ICPPW.2010.64
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-09-13
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Prefetching Context Accuracy History Benchmark testing Radiation detectors Indexes high-end computing data prefetching context-based prefetching memory access performance
Content Type	Text
Resource Type	Article

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