skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Improving the Effectiveness of Context-based Prefetching with Multi-order Analysis

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 context-based 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 SPEC-CPU2006 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.

Authors:
 [1];  [2];  [2];  [2]
  1. ORNL
  2. Illinois Institute of Technology
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE Office of Science (SC)
OSTI Identifier:
989730
DOE Contract Number:  
DE-AC05-00OR22725
Resource Type:
Conference
Resource Relation:
Conference: International Workshop on Parallel Programming Models and Systems Software for High-End Computing, San Diego, CA, USA, 20100912, 20100912
Country of Publication:
United States
Language:
English
Subject:
99 GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE; BENCHMARKS; PERFORMANCE; PROGRAMMING; SIMULATION; TESTING; COMPUTERS

Citation Formats

Chen, Yong, Zhu, Huaiyu, Jin, Hui, and Sun, Xian-He. Improving the Effectiveness of Context-based Prefetching with Multi-order Analysis. United States: N. p., 2010. Web.
Chen, Yong, Zhu, Huaiyu, Jin, Hui, & Sun, Xian-He. Improving the Effectiveness of Context-based Prefetching with Multi-order Analysis. United States.
Chen, Yong, Zhu, Huaiyu, Jin, Hui, and Sun, Xian-He. Fri . "Improving the Effectiveness of Context-based Prefetching with Multi-order Analysis". United States.
@article{osti_989730,
title = {Improving the Effectiveness of Context-based Prefetching with Multi-order Analysis},
author = {Chen, Yong and Zhu, Huaiyu and Jin, Hui and Sun, Xian-He},
abstractNote = {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 context-based 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 SPEC-CPU2006 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.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2010},
month = {1}
}

Conference:
Other availability
Please see Document Availability for additional information on obtaining the full-text document. Library patrons may search WorldCat to identify libraries that hold this conference proceeding.

Save / Share: