Processing of Range Query Using SIMD and GPU

被引:0
|
作者
Bednar, Pavel [1 ]
Gajdos, Petr [1 ]
Kratky, Michal [1 ]
Chovanec, Peter [1 ]
机构
[1] VSB Tech Univ Ostrava, Dept Comp Sci, Ostrava, Czech Republic
关键词
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Onedimensional or multidimensional range query is one of the most important query of physical implementation of DBMS. The number of compared items (of a data structure) can be enormous especially for lower selectivity of the range query. The number of compare operations increases for more complex items (or tuples) with the longer length, e.g. words stored in a B-tree. Due to the possibly high number of compare operations executed during the range query processing, we can take into account hardware devices providing a parallel task computation like CPU's SIMD or GPU. In this paper, we show the performance and scalability of sequential, index, CPU's SIMD, and GPU variants of the range query algorithm. These results make possible a future integration of these computation devices into a DBMS kernel.
引用
收藏
页码:13 / 25
页数:13
相关论文
共 50 条
  • [41] Approximate range query processing in spatial network databases
    Haidar AL-Khalidi
    Zainab Abbas
    Maytham Safar
    Multimedia Systems, 2013, 19 : 151 - 161
  • [42] Range sum query processing in parallel data warehouses
    Li, JZ
    Gao, H
    PARALLEL AND DISTRIBUTED COMPUTING, APPLICATIONS AND TECHNOLOGIES, PDCAT'2003, PROCEEDINGS, 2003, : 877 - 881
  • [43] Efficient and Oblivious Query Processing for Range and kNN Queries
    Chang, Zhao
    Xie, Dong
    Li, Feifei
    Phillips, Jeff M.
    Balasubramonian, Rajeev
    2022 IEEE 38TH INTERNATIONAL CONFERENCE ON DATA ENGINEERING (ICDE 2022), 2022, : 1487 - 1488
  • [44] Approximate range query processing in spatial network databases
    AL-Khalidi, Haidar
    Abbas, Zainab
    Safar, Maytham
    MULTIMEDIA SYSTEMS, 2013, 19 (02) : 151 - 161
  • [45] Efficient Multi-range Query Processing on Trajectories
    Yadamjav, Munkh-Erdene
    Choudhury, Farhana M.
    Bao, Zhifeng
    Samet, Hanan
    CONCEPTUAL MODELING, ER 2018, 2018, 11157 : 269 - 285
  • [46] A Differentially Private Index for Range Query Processing in Clouds
    Sahin, Cetin
    Allard, Tristan
    Akbarinia, Reza
    El Abbadi, Amr
    Pacitti, Esther
    2018 IEEE 34TH INTERNATIONAL CONFERENCE ON DATA ENGINEERING (ICDE), 2018, : 857 - 868
  • [47] Strategies of SIMD computing for image coding in GPU
    Enfedaque, Pablo
    Auli-Llinas, Francesc
    Moure, Juan C.
    2015 IEEE 22nd International Conference on High Performance Computing (HiPC), 2015, : 345 - 354
  • [48] Understanding the SIMD Efficiency of Graph Traversal on GPU
    Cheng, Yichao
    An, Hong
    Chen, Zhitao
    Li, Feng
    Wang, Zhaohui
    Jiang, Xia
    Peng, Yi
    ALGORITHMS AND ARCHITECTURES FOR PARALLEL PROCESSING, ICA3PP 2014, PT I, 2014, 8630 : 42 - 56
  • [49] VIP: A SIMD vectorized analytical query engine
    Polychroniou, Orestis
    Ross, Kenneth A.
    VLDB JOURNAL, 2020, 29 (06): : 1243 - 1261
  • [50] In-memory k Nearest Neighbor GPU-based Query Processing
    Velentzas, Polychronis
    Vassilakopoulos, Michael
    Corral, Antonio
    PROCEEDINGS OF THE 6TH INTERNATIONAL CONFERENCE ON GEOGRAPHICAL INFORMATION SYSTEMS THEORY, APPLICATIONS AND MANAGEMENT (GISTAM), 2020, : 310 - 317