面向大数据应用的混合内存架构特征分析

李鑫; 陈璇; 黄志球

doi:10.11959/j.issn.2096-0271.2018031

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面向大数据应用的混合内存架构特征分析

研究 | 更新时间：2024-06-03

- 面向大数据应用的混合内存架构特征分析
- Analysis on hybrid memory architecture for big data application
- 大数据 2018年4卷第3期页码：2018031
- 作者机构：
  
  1. 南京航空航天大学计算机科学与技术学院，江苏南京 211106
  2. 南京航空航天大学自动化学院，江苏南京 211106
- 作者简介：
  
  [ "李鑫（1987-），男，博士，南京航空航天大学计算机科学与技术学院讲师、硕士生导师，中国计算机学会（CCF）会员，主要研究方向为云计算、数据管理与分析、内存计算等。" ]
  [ "陈璇（1996-），女，南京航空航天大学自动化学院本科生，主要研究方向为云计算与大数据。" ]
  [ "黄志球（1965-），男，博士，南京航空航天大学计算机科学与技术学院教授、博士生导师，CCF杰出会员，主要研究方向为嵌入式软件安全性、形式化验证技术、隐私保护等。" ]
- 基金信息：
  
  国家高技术研究发展计划（“863”计划）基金资助项目;The National High Technology Research and Development Program of China(2015AA01530);江苏省自然科学基金资助项目;Jiangsu Natural Science Foundation(BK20160813)
- DOI：10.11959/j.issn.2096-0271.2018031
  中图分类号： TP302.7
- 网络首发：2018-05，
  
  纸质出版：2018-05-15
- 稿件说明：
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李鑫, 陈璇, 黄志球. 面向大数据应用的混合内存架构特征分析[J]. 大数据, 2018,4(3):2018031.

Xin LI, Xuan CHEN, Zhiqiu HUANG. Analysis on hybrid memory architecture for big data application[J]. Big Data Research, 2018, 4(3): 2018031.
李鑫, 陈璇, 黄志球. 面向大数据应用的混合内存架构特征分析[J]. 大数据, 2018,4(3):2018031. DOI： 10.11959/j.issn.2096-0271.2018031.

Xin LI, Xuan CHEN, Zhiqiu HUANG. Analysis on hybrid memory architecture for big data application[J]. Big Data Research, 2018, 4(3): 2018031. DOI： 10.11959/j.issn.2096-0271.2018031.

摘要

受限于DRAM的扩展性，大数据分析及相关应用性能难以有效提升。新型非易失性存储器凭借其非易失性、高存储密度、低能耗等优点，为大数据应用的性能与效率提升带来了契机。以新型非易失性存储器为基础，阐述PCM/DRAM混合存储架构，通过对该混合存储架构在性能优化、能耗优化、内存管理策略等方面的综述分析，详述了混合存储架构在大数据应用方面的优势及可行性，总结了现有研究工作的缺陷，展望了PCM/DRAM混合内存后续的研究方向。

Abstract

Due to the limited scalability of DRAM

it is hard to optimize the performance of big data analysis and the big data applications.The new non-volatile memory (NVM) brings the opportunity to improve the performance and efficiency for big data applications

which benefits by the advantages of NVM

including its non-volatile

high storage density

and low power consumption.The PCM/DRAM hybrid memory architecture based on the non-volatile memory was analyzed.The feasibility and advantages of hybrid memory for big data applications through the analysis on the optimization of performance

energy consumption and memory management strategies for hybrid memory architecture were demonstrated.The defects in existing work were summarized and the potential research field in PCM/DRAM hybrid memory architecture was discussed.

关键词

Keywords

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