A Survey of Recent Prefetching Techniques for Processor Caches

Author:

Mittal Sparsh1ORCID

Affiliation:

1. Oak Ridge National Laboratory, Tennessee

Abstract

As the trends of process scaling make memory systems an even more crucial bottleneck, the importance of latency hiding techniques such as prefetching grows further. However, naively using prefetching can harm performance and energy efficiency and, hence, several factors and parameters need to be taken into account to fully realize its potential. In this article, we survey several recent techniques that aim to improve the implementation and effectiveness of prefetching. We characterize the techniques on several parameters to highlight their similarities and differences. The aim of this survey is to provide insights to researchers into working of prefetching techniques and spark interesting future work for improving the performance advantages of prefetching even further.

Funder

Advanced Scientific Computing Research

U.S. Department of Energy

Office of Science

Publisher

Association for Computing Machinery (ACM)

Subject

General Computer Science,Theoretical Computer Science

Cited by 55 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. BOOM-Explorer: RISC-V BOOM Microarchitecture Design Space Exploration;ACM Transactions on Design Automation of Electronic Systems;2023-12-18

2. Application and user-specific data prefetching and parallel read algorithms for distributed file systems;Cluster Computing;2023-10-28

3. Decoupled Vector Runahead;56th Annual IEEE/ACM International Symposium on Microarchitecture;2023-10-28

4. A Survey of Memory-Centric Energy Efficient Computer Architecture;IEEE Transactions on Parallel and Distributed Systems;2023-10

5. Optimizing performance and energy across problem sizes through a search space exploration and machine learning;Journal of Parallel and Distributed Computing;2023-10

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3