Enabling CXL Memory Expansion for In-Memory Database Management Systems

被引:13
|
作者
Ahn, Minseon [1 ]
Lee, Donghun [1 ]
Kim, Jungmin [1 ]
Rebholz, Oliver [2 ]
Chang, Andrew [3 ]
Gim, Jongmin [3 ]
Jung, Jaemin [3 ]
Pham, Vincent [3 ]
Malladi, Krishna T. [3 ]
Ki, Yang Seok [3 ]
机构
[1] SAP Labs Korea, Seoul, South Korea
[2] SAP SE, Walldorf, Baden Wurttembe, Germany
[3] Samsung Semicond Inc, San Jose, CA USA
关键词
CXL; Compute Express Link; In-Memory Database; DBMS; Database Management Systems;
D O I
10.1145/3533737.3535090
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
Limited memory volume is always a performance bottleneck in an in-memory database management system (IMDBMS) as the data size keeps increasing. To overcome the physical memory limitation, heterogeneous and disaggregated computing platforms are proposed, such as Gen-Z, CCIX, OpenCAPI, and CXL. In this work, we introduce flexible CXL memory expansion using a CXL type 3 prototype and evaluate its performance in an IMDBMS. Our evaluation shows that CXL memory devices interfaced with PCIe Gen5 are appropriate for memory expansion with nearly no throughput degradation in OLTP workloads and less than 8% throughput degradation in OLAP workloads. Thus, CXL memory is a good candidate for memory expansion with lower TCO in IMDBMSs.
引用
收藏
页数:5
相关论文
共 50 条
  • [21] Configurable Memory With a Multilevel Shared Structure Enabling In-Memory Computing
    Zhao, Yue
    Lin, Zhiting
    Wu, Xiulong
    Zhao, Qiang
    Lu, Wenjuan
    Peng, Chunyu
    Tong, Zhongzhen
    Chen, Junning
    IEEE TRANSACTIONS ON VERY LARGE SCALE INTEGRATION (VLSI) SYSTEMS, 2022, 30 (05) : 566 - 578
  • [22] Predicting In-Memory Database Performance for Automating Cluster Management Tasks
    Schaffner, Jan
    Eckart, Benjamin
    Jacobs, Dean
    Schwarz, Christian
    Plattner, Hasso
    Zeier, Alexander
    IEEE 27TH INTERNATIONAL CONFERENCE ON DATA ENGINEERING (ICDE 2011), 2011, : 1264 - 1275
  • [23] A Highly Scalable Index Structure for Multicore In-Memory Database Systems
    Mitake, Hitoshi
    Yamada, Hiroshi
    Nakajima, Tatsuo
    INTELLIGENT DISTRIBUTED COMPUTING XIII, 2020, 868 : 210 - 217
  • [24] Consistent Snapshot Algorithms for In-Memory Database Systems: Experiments and Analysis
    Li, Liang
    Wang, Guoren
    Wu, Gang
    Yuan, Ye
    2018 IEEE 34TH INTERNATIONAL CONFERENCE ON DATA ENGINEERING (ICDE), 2018, : 1284 - 1287
  • [25] DWARM: A wear-aware memory management scheme for in-memory file systems
    Wu, Lin
    Zhuge, Qingfeng
    Sha, Edwin Hsing-Mean
    Chen, Xianzhang
    Cheng, Linfeng
    FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE, 2018, 88 : 1 - 15
  • [26] Eager Memory Management for In-Memory Data Analytics
    Jang, Hakbeom
    Bae, Jonghyun
    Ham, Tae Jun
    Lee, Jae W.
    IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS, 2019, E102D (03): : 632 - 636
  • [27] Elastic Pipelining in an In-Memory Database Cluster
    Wang, Li
    Zhou, Minqi
    Zhang, Zhenjie
    Yang, Yin
    Zhou, Aoying
    Bitton, Dina
    SIGMOD'16: PROCEEDINGS OF THE 2016 INTERNATIONAL CONFERENCE ON MANAGEMENT OF DATA, 2016, : 1279 - 1294
  • [28] In-memory database acceleration on FPGAs: a survey
    Jian Fang
    Yvo T. B. Mulder
    Jan Hidders
    Jinho Lee
    H. Peter Hofstee
    The VLDB Journal, 2020, 29 : 33 - 59
  • [29] HIG - An In-memory Database Platform Enabling Real-time Analyses of Genome Data
    Schapranow, Matthieu-P.
    Plattner, Hasso
    2013 IEEE INTERNATIONAL CONFERENCE ON BIG DATA, 2013,
  • [30] MemTest: A novel benchmark for in-memory database
    Jin, Cheqing (cqjin@sei.ecnu.edu.cn), 1600, Springer Verlag (8807):