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Title: Global-Aware and Multi-Order Context-Based Prefetching for High-Performance Processors

Abstract

Data prefetching is widely used in high-end computing systems to accelerate data accesses and to bridge the increasing performance gap between processor and memory. Context-based prefetching has become a primary focus of study in recent years due to its general applicability. However, current context-based prefetchers only adopt the context analysis of a single order, which suffers from low prefetching coverage and thus limits the overall prefetching effectiveness. Also, existing approaches usually consider the context of the address stream from a single instruction but not the context of the address stream from all instructions, which further limits the context-based prefetching effectiveness. In this study, we propose a new context-based prefetcher called the Global-aware and Multi-order Context-based (GMC) prefetcher. The GMC prefetcher uses multi-order, local and global context analysis to increase prefetching coverage while maintaining prefetching accuracy. In extensive simulation testing of the SPEC-CPU2006 benchmarks with an enhanced CMP$im simulator, the proposed GMC prefetcher was shown to outperform existing prefetchers and to reduce the data-access latency effectively. The average Instructions Per Cycle (IPC) improvement of SPEC CINT2006 and CFP2006 benchmarks with GMC prefetching was over 55% and 44% respectively.

Authors:
 [1];  [2];  [1];  [2];  [2]
  1. ORNL
  2. Illinois Institute of Technology
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Center for Computational Sciences
Sponsoring Org.:
USDOE Office of Science (SC)
OSTI Identifier:
1024306
DOE Contract Number:  
DE-AC05-00OR22725
Resource Type:
Journal Article
Journal Name:
International Journal of High Performance Computing Applications
Additional Journal Information:
Journal Name: International Journal of High Performance Computing Applications; Journal ID: ISSN 1094-3420
Country of Publication:
United States
Language:
English
Subject:
99 GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE; ACCURACY; BENCHMARKS; PERFORMANCE; SIMULATION; TESTING

Citation Formats

Chen, Yong, Zhu, Huaiyu, Roth, Philip C, Jin, Hui, and Sun, Xian-He. Global-Aware and Multi-Order Context-Based Prefetching for High-Performance Processors. United States: N. p., 2011. Web. doi:10.1177/1094342010394386.
Chen, Yong, Zhu, Huaiyu, Roth, Philip C, Jin, Hui, & Sun, Xian-He. Global-Aware and Multi-Order Context-Based Prefetching for High-Performance Processors. United States. doi:10.1177/1094342010394386.
Chen, Yong, Zhu, Huaiyu, Roth, Philip C, Jin, Hui, and Sun, Xian-He. Sat . "Global-Aware and Multi-Order Context-Based Prefetching for High-Performance Processors". United States. doi:10.1177/1094342010394386.
@article{osti_1024306,
title = {Global-Aware and Multi-Order Context-Based Prefetching for High-Performance Processors},
author = {Chen, Yong and Zhu, Huaiyu and Roth, Philip C and Jin, Hui and Sun, Xian-He},
abstractNote = {Data prefetching is widely used in high-end computing systems to accelerate data accesses and to bridge the increasing performance gap between processor and memory. Context-based prefetching has become a primary focus of study in recent years due to its general applicability. However, current context-based prefetchers only adopt the context analysis of a single order, which suffers from low prefetching coverage and thus limits the overall prefetching effectiveness. Also, existing approaches usually consider the context of the address stream from a single instruction but not the context of the address stream from all instructions, which further limits the context-based prefetching effectiveness. In this study, we propose a new context-based prefetcher called the Global-aware and Multi-order Context-based (GMC) prefetcher. The GMC prefetcher uses multi-order, local and global context analysis to increase prefetching coverage while maintaining prefetching accuracy. In extensive simulation testing of the SPEC-CPU2006 benchmarks with an enhanced CMP$im simulator, the proposed GMC prefetcher was shown to outperform existing prefetchers and to reduce the data-access latency effectively. The average Instructions Per Cycle (IPC) improvement of SPEC CINT2006 and CFP2006 benchmarks with GMC prefetching was over 55% and 44% respectively.},
doi = {10.1177/1094342010394386},
journal = {International Journal of High Performance Computing Applications},
issn = {1094-3420},
number = ,
volume = ,
place = {United States},
year = {2011},
month = {1}
}