A Near-Data Processing Server Architecture and Its Impact on Data Center Applications

被引:2
作者
Song, Xiaojia [1 ]
Xie, Tao [1 ]
Fischer, Stephen [2 ]
机构
[1] San Diego State Univ, 5500 Campanile Dr, San Diego, CA 92182 USA
[2] Samsung Semicond, 3655 N 1st St, San Jose, CA 95134 USA
来源
HIGH PERFORMANCE COMPUTING, ISC HIGH PERFORMANCE 2019 | 2019年 / 11501卷
基金
美国国家科学基金会;
关键词
Near data processing; Data center server; FPGA; ARM embedded processor; Data-intensive; Compute-intensive;
D O I
10.1007/978-3-030-20656-7_5
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
Existing near-data processing (NDP) techniques have demonstrated their strength for some specific data-intensive applications. However, they might be inadequate for a data center server, which normally needs to perform a diverse range of applications from data-intensive to compute-intensive. How to develop a versatile NDP-powered server to support various data center applications remains an open question. Further, a good understanding of the impact of NDP on data center applications is still missing. For example, can a compute-intensive application also benefit from NDP? Which type of NDP engine is a better choice, an FPGA-based engine or an ARM-based engine? To address these issues, we first propose a new NDP server architecture that tightly couples each SSD with a dedicated NDP engine to fully exploit the data transfer bandwidth of an SSD array. Based on the architecture, two NDP servers ANS (ARM-based NDP Server) and FNS (FPGA-based NDP Server) are introduced. Next, we implement a single-engine prototype for each of them. Finally, we measure performance, energy efficiency, and cost/performance ratio of six typical data center applications running on the two prototypes. Some new findings have been observed.
引用
收藏
页码:81 / 98
页数:18
相关论文
共 27 条
  • [1] [Anonymous], 2009, PROC IEEE C COMPUT V
  • [2] [Anonymous], 2016, ACM SIGARCH COMPUTER
  • [3] Asanovic Krste, 2014, USENIX FAST, V13
  • [4] Cho Sangyeun, 2013, International Conference on International Conference on Supercomputing, P91, DOI [10.1145/2464996.2465003, DOI 10.1145/2464996.2465003]
  • [5] CNXSoft, 2015, CNXSOFT ALLWINNER A6
  • [6] Davidson G.S., 2006, TECHNICAL REPORT
  • [7] Minerva: Accelerating Data Analysis in Next-Generation SSDs
    De, Arup
    Gokhale, Maya
    Gupta, Rajesh
    Swanson, Steven
    [J]. 2013 IEEE 21ST ANNUAL INTERNATIONAL SYMPOSIUM ON FIELD-PROGRAMMABLE CUSTOM COMPUTING MACHINES (FCCM), 2013, : 9 - 16
  • [8] Fidus Systems Inc, 2017, FID SID 100
  • [9] Practical Near-Data Processing for In-memory Analytics Frameworks
    Gao, Mingyu
    Ayers, Grant
    Kozyrakis, Christos
    [J]. 2015 INTERNATIONAL CONFERENCE ON PARALLEL ARCHITECTURE AND COMPILATION (PACT), 2015, : 113 - 124
  • [10] Biscuit: A Framework for Near-Data Processing of Big Data Workloads
    Gu, Boncheol
    Yoon, Andre S.
    Bae, Duck-Ho
    Jo, Insoon
    Lee, Jinyoung
    Yoon, Jonghyun
    Kang, Jeong-Uk
    Kwon, Moonsang
    Yoon, Chanho
    Cho, Sangyeun
    Jeong, Jaeheon
    Chang, Duckhyun
    [J]. 2016 ACM/IEEE 43RD ANNUAL INTERNATIONAL SYMPOSIUM ON COMPUTER ARCHITECTURE (ISCA), 2016, : 153 - 165