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
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 50 条
  • [1] Two Reconfigurable NDP Servers: Understanding the Impact of Near-Data Processing on Data Center Applications
    Song, Xiaojia
    Xie, Tao
    Fischer, Stephen
    [J]. ACM TRANSACTIONS ON STORAGE, 2021, 17 (04)
  • [2] An Architecture for Near-Data Processing Systems
    Vermij, Erik
    Hagleitner, Christoph
    Fiorin, Leandro
    Jongerius, Rik
    van Lunteren, Jan
    Bertels, Koen
    [J]. PROCEEDINGS OF THE ACM INTERNATIONAL CONFERENCE ON COMPUTING FRONTIERS (CF'16), 2016, : 357 - 360
  • [3] NEAR-DATA PROCESSING
    Balasubramonian, Rajeev
    Grot, Boris
    [J]. IEEE MICRO, 2016, 36 (01) : 4 - 5
  • [4] Jarvis: Large-scale Server Monitoring with Adaptive Near-data Processing
    Sandur, Atul
    Park, ChanHo
    Volos, Stavros
    Agha, Gul
    Jeon, Myeongjae
    [J]. 2022 IEEE 38TH INTERNATIONAL CONFERENCE ON DATA ENGINEERING (ICDE 2022), 2022, : 1408 - 1422
  • [5] Overcoming Challenges to Near-Data Processing
    Jayasena, Nuwan
    [J]. IEEE MICRO, 2016, 36 (01) : 8 - 9
  • [6] An Architecture for Integrated Near-Data Processors
    Vermij, Erik
    Fiorin, Leandro
    Jongerius, Rik
    Hagleitner, Christoph
    Van Lunteren, Jan
    Bertels, Koen
    [J]. ACM TRANSACTIONS ON ARCHITECTURE AND CODE OPTIMIZATION, 2017, 14 (03)
  • [7] Near-Data Processing of Neural Networks
    Chen, Yunji
    Tao, Jinhua
    [J]. IEEE MICRO, 2016, 36 (01) : 9 - 10
  • [8] Optimizing Near-Data Processing for Spark
    Rachuri, Sri Pramodh
    Gantasala, Arun
    Emanuel, Prajeeth
    Gandhi, Anshul
    Foley, Robert
    Puhov, Peter
    Gkountouvas, Theodoros
    Lei, Hui
    [J]. 2022 IEEE 42ND INTERNATIONAL CONFERENCE ON DISTRIBUTED COMPUTING SYSTEMS (ICDCS 2022), 2022, : 636 - 646
  • [9] GraNDe: Efficient Near-Data Processing Architecture for Graph Neural Networks
    Yun, Sungmin
    Nam, Hwayong
    Park, Jaehyun
    Kim, Byeongho
    Ahn, Jung Ho
    Lee, Eojin
    [J]. IEEE TRANSACTIONS ON COMPUTERS, 2024, 73 (10) : 2391 - 2404
  • [10] NDPGNN: A Near-Data Processing Architecture for GNN Training and Inference Acceleration
    Wang, Haoyang
    Zhang, Shengbing
    Fan, Xiaoya
    Yang, Zhao
    Zhang, Meng
    [J]. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2024, 43 (11) : 3997 - 4008