Self-learnable cluster-based prefetching method for DRAM-flash hybrid main memory architecture

Su Kyung Yoon, Young Sun Youn, Bernd Burgstaller, Shin Dug Kim

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

This article presents a novel prefetching mechanism for memory-intensive workloads used in large-scale data centers. We design a negative-AND-flash/dynamic random-access memory (DRAM) hybrid memory architecture as a cost-effective memory architecture to resolve the scalability and power consumption problems of a DRAM-based model. A smart prefetching mechanism based on a cluster-management scheme to cope with dynamically varying and complex access patterns of any given application is designed for maximizing the performance of the DRAM. In this article, we propose a new concept for page management, called a cluster, which prefetches data in our hybrid memory architecture. The cluster management is based on a self-learning scheme on dynamically changeable access patterns by considering any correlation between missed pages. Experimental results show that the overall performance is significantly improved in relation to hit rate, execution time, and energy consumption. Namely, our proposed model can enhance the hit rate by 15% and reduce the execution time by 1.75 times. In addition, we can save energy consumption by around 48% by cutting the number of flushed pages to about an eighth of that in a conventional system.

Original languageEnglish
Article number10
JournalACM Journal on Emerging Technologies in Computing Systems
Volume15
Issue number1
DOIs
Publication statusPublished - 2019 Jan

Bibliographical note

Funding Information:
This research was supported by 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) by an Industry-Academy joint research program between Samsung Electronics and Yonsei University, and by the Graduate School of YONSEI University Research Scholarship Grants in 2017. Authors’ addresses: S.-K. Yoon, Y.-S. Youn, B. Burgstaller, and S.-D. Kim, Department of Computer Science, Yonsei University, Seoul, Korea; emails: {sk.yoon, ys.youn, bburg, sdkim}@yonsei.ac.kr. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org. © 2019 Association for Computing Machinery. 1550-4832/2019/01-ART10 $15.00 https://doi.org/10.1145/3284932

All Science Journal Classification (ASJC) codes

  • Software
  • Hardware and Architecture
  • Electrical and Electronic Engineering

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