Cart (Loading....) | Create Account
Close category search window

An Adaptive Data Prefetcher for High-Performance Processors

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Yong Chen ; Dept. of Comput. Sci., Illinois Inst. of Technol., Chicago, IL, USA ; Huaiyu Zhu ; Xian-He Sun

While computing speed continues increasing rapidly, data-access technology is lagging behind. Data-access delay, not the processor speed, becomes the leading performance bottleneck of high-end/high-performance computing. Prefetching is an effective solution to masking the gap between computing speed and data-access speed. Existing works of prefetching, however, are very conservative in general, due to the computing power consumption concern of the past. They suffer in effectiveness especially when applications' access pattern changes. In this study, we propose an Algorithm-level Feedback-controlled Adaptive (AFA) data prefetcher to address these issues. The AFA prefetcher is based on the Data-Access History Cache, a hardware structure that is specifically designed for data prefetching. It provides an algorithm-level adaptation and is capable of dynamically adapting to appropriate prefetching algorithms at runtime. We have conducted extensive simulation testing with Simple Scalar simulator to validate the design and to illustrate the performance gain. The simulation results show that AFA prefetcher is effective and achieves considerable IPC (Instructions Per Cycle) improvement in average.

Published in:

Cluster, Cloud and Grid Computing (CCGrid), 2010 10th IEEE/ACM International Conference on

Date of Conference:

17-20 May 2010

Need Help?

IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.