Regression prefetcher with preprocessing for DRAM-PCM hybrid main memory

Ji Tae Yun, Su Kyung Yoon, Jeong Geun Kim, Bernd Burgstaller, Shin Dug Kim

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

This research is to design an effective hybrid main memory structure for graph processing applications, because it is quite expensive to use only high-speed DRAM for such applications. Thus, we propose a DRAM-PCM hybrid main memory structure to reduce the cost and energy consumption and design regression prefetch scheme to cope with irregular access patterns in large graph processing workloads. In addition, the prefetch includes preprocessing algorithm to maximize prefetching performance. Our experimental evaluation shows a performance improvement of 36 percent over a conventional DRAM model, 15 percent over existing prefetch models such as GHB/PC, SMS, and AMPM, and 6 percent over the latest model.

Original languageEnglish
Pages (from-to)163-166
Number of pages4
JournalIEEE Computer Architecture Letters
Volume17
Issue number2
DOIs
Publication statusPublished - 2018 Jul 1

Bibliographical note

Funding Information:
This research was partially supported by the Next Generation Information Computing Development Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (NRF-2015M3C4A7065522) and by an Industry-Academy joint research program between Sam-sung Electronics and Yonsei University.

Publisher Copyright:
© 2002-2011 IEEE.

All Science Journal Classification (ASJC) codes

  • Hardware and Architecture

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