面向大数据的异构内存系统

doi:10.11959/j.issn.2096-0271.2018037

Abstract

Abstract:

Big data applications put more pressure on memory system in the aspect of capacity and power consumption.However,limited by the shortcomings of DRAM and non-volatile memory(NVM),memory consists of single medium like DRAM or NVM is not competent for the requirements of big data applications.Thus,how to effectively design,efficiently manage and accurately evaluate the hybrid memories consist of DRAM and NVM are the major challenges that the academia and industry face today.The challenges of hybrid memory systems for big data processing from the perspective of computer architecture,system software,programming model and application were analyzed,and several solutions and optimizations were correspondingly provided,such as on-chip cache management,parallel processing,memory access scheduling,energy management,virtual-to-physical address translation,object-level memory allocation and migration mechanisms.Meanwhile,a number of prototypes to validate the effectiveness and efficiency of these proposals were developed.

Key words: in-memory processing, hybrid memory, big data, non-volatile memory

CLC Number:

TP31

Xiaoyuan WANG, Xiaofei LIAO, Haikun LIU, Hai JIN. Big data oriented hybrid memory systems[J]. Big Data Research, 2018, 4(4): 15-34.

Figures/Tables 15

References 23

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参数	单元面积（F²）	读操作时延/ns	写操作时延/ns	耐久性/次	写操作功耗/(nJ·b^-1)	空闲功耗	数据保持/年
DRAM	6~8	<10	<10	N/A	~0.1	高	刷新
PCM	4~8	10~100	20~120	10⁸~10¹²	<1	低	>10
自旋力矩存储（STT-RAM）	16~60	2~20	5~35	10¹²~10¹⁵	1.6~5	低	>10
阻变式存储器（RRAM）	4~14	10~50	10~50	10⁸~10¹⁰	~0.1	低	10
铁电随机存储器（FeRAM）	15~34	5~10	5~10	10¹²~10¹⁴	<1	低	10

Big data oriented hybrid memory systems

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