Parallel Strategy for the Large-Scale Data Streams Processing

被引：0
|
作者：
Yuan, Ya-Juan [1 ]
Ma, Guo-Jie [1 ]
机构：
[1] Cang Zhou Med Coll, Cangzhou, Peoples R China
来源：
PROCEEDINGS OF THE 2016 INTERNATIONAL CONFERENCE ON COMPUTER ENGINEERING AND INFORMATION SYSTEMS | 2016年 / 52卷
关键词：
graphics processing units; parallel strategy; power efficiency; power model;
D O I：
暂无
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
Large-scale data streams processing is import to data processing application. So we need to investigate the parallel strategy for the Large-scale data streams processing. Here we propose two parallel strategies to handle data streams in real time, and consider the power efficiency as an important factor to the parallel strategies. We present a method to quantify the power efficiency for data streams during the computing. Finally, we compare the two parallel strategies on a large quantity of real stream data. The experiments prove the accuracy of analysis on power efficiency.
引用
收藏
页码：232 / 234
页数：3
相关论文
共 50 条
  • [1] A Hybrid Parallel Processing Strategy for Large-Scale DEA Computation
    Chang, Shengqing
    Ding, Jingjing
    Feng, Chenpeng
    Wang, Ruifeng
    [J]. COMPUTATIONAL ECONOMICS, 2024, 63 (06) : 2325 - 2349
  • [2] An Efficient Strategy for Large-Scale CORS Data Processing
    Xiong, Bolin
    Huang, Dingfa
    [J]. CHINA SATELLITE NAVIGATION CONFERENCE (CSNC) 2016 PROCEEDINGS, VOL I, 2016, 388 : 213 - 225
  • [3] Extension of Parallel Primitives and Their Applications to Large-Scale Data Processing
    Nakano, Masashi
    Chang, Qiong
    Miyazaki, Jun
    [J]. DATABASE AND EXPERT SYSTEMS APPLICATIONS, PT II, DEXA 2024, 2024, 14911 : 248 - 253
  • [4] Designing Parallel Data Processing for Large-Scale Sensor Orchestration
    Kabac, Milan
    Consel, Charles
    [J]. 2016 INT IEEE CONFERENCES ON UBIQUITOUS INTELLIGENCE & COMPUTING, ADVANCED & TRUSTED COMPUTING, SCALABLE COMPUTING AND COMMUNICATIONS, CLOUD AND BIG DATA COMPUTING, INTERNET OF PEOPLE, AND SMART WORLD CONGRESS (UIC/ATC/SCALCOM/CBDCOM/IOP/SMARTWORLD), 2016, : 57 - 65
  • [5] LARGE-SCALE PARALLEL PROCESSING SYSTEMS
    SIEGEL, HJ
    SCHWEDERSKI, T
    MEYER, DG
    HSU, WT
    [J]. MICROPROCESSORS AND MICROSYSTEMS, 1987, 11 (01) : 3 - 20
  • [6] HPPQ: A Parallel Package Queries Processing Approach for Large-Scale Data
    Meihui Shi
    Derong Shen
    Tiezheng Nie
    Yue Kou
    Ge Yu
    [J]. Big Data Mining and Analytics, 2018, 1 (02) : 146 - 159
  • [7] Distributed frameworks and parallel algorithms for processing large-scale geographic data
    Hawick, KA
    Coddington, PD
    James, HA
    [J]. PARALLEL COMPUTING, 2003, 29 (10) : 1297 - 1333
  • [8] Designing parallel data processing for enabling large-scale sensor applications
    Milan Kabáč
    Charles Consel
    Nic Volanschi
    [J]. Personal and Ubiquitous Computing, 2017, 21 : 457 - 473
  • [9] HPPQ: A Parallel Package Queries Processing Approach for Large-Scale Data
    Shi, Meihui
    Shen, Derong
    Nie, Tiezheng
    Kou, Yue
    Yu, Ge
    [J]. BIG DATA MINING AND ANALYTICS, 2018, 1 (02): : 146 - 159
  • [10] Designing parallel data processing for enabling large-scale sensor applications
    Kabac, Milan
    Consel, Charles
    Volanschi, Nic
    [J]. PERSONAL AND UBIQUITOUS COMPUTING, 2017, 21 (03) : 457 - 473
12345