Global-aware and multi-order context-based prefetching for high-performance processors

  • Authors:
  • Yong Chen;Huaiyu Zhu;Philip C. Roth;Hui Jin;Xian-He Sun

  • Affiliations:
  • Department of Computer Science, Texas Tech University, USA;Department of Computer Science, University of Illinois at Urbana Champaign, USA;Computer Science and Mathematics Division, Oak Ridge National Laboratory, USA;Department of Computer Science, Illinois Institute of Technology, USA;Department of Computer Science, Illinois Institute of Technology, USA

  • Venue:
  • International Journal of High Performance Computing Applications
  • Year:
  • 2011

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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.