A high-bandwidth memory pipeline for wide issue processors

Sangyeun Cho, Pen Chung Yew, Gyungho Lee

Research output: Contribution to journalArticle

8 Citations (Scopus)

Abstract

Providing adequate data bandwidth is extremely important for a future wide-issue processor to achieve its full performance potential. Adding a large number of ports to a data cache, however, becomes increasingly inefficient and can add to the hardware complexity significantly. This paper takes an alternative or complementary approach for providing more data bandwidth, called data decoupling. This paper especially studies an interesting, yet less explored, behavior of memory access instructions, called access region locality, which is concerned with each static memory instruction and its range of access locations at runtime. Our experimental study using a set of SPEC95 benchmark programs shows that most memory access instructions reference a single region at runtime. Also shown is that it is possible to accurately predict the access region of a memory instruction at runtime by scrutinizing the addressing mode of the instruction and the past access history of it. We describe and evaluate a wide-issue superscalar processor with two distinct sets of memory pipelines and caches, driven by the access region predictor. Experimental results indicate that the proposed mechanism is very effective in providing high memory bandwidth to the processor, resulting in comparable or better performance than a conventional memory design with a heavily multiported data cache that can lead to much higher hardware complexity.

Original languageEnglish
Pages (from-to)709-723
Number of pages15
JournalIEEE Transactions on Computers
Volume50
Issue number7
DOIs
Publication statusPublished - 2001 Jul
Externally publishedYes

Keywords

  • Data bandwidth
  • Data locality
  • Data stream partitioning
  • Instruction level parallelism
  • Multiported data cache
  • Runtime stack

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science
  • Hardware and Architecture
  • Computational Theory and Mathematics

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