Parallel Matrix Multiplication for Business Applications

被引:0
|
作者
Qasem, Mais Haj [1 ]
Qatawneh, Mohammad [1 ]
机构
[1] Univ Jordan, Comp Sci Dept, Amman, Jordan
来源
APPLIED COMPUTATIONAL INTELLIGENCE AND MATHEMATICAL METHODS: COMPUTATIONAL METHODS IN SYSTEMS AND SOFTWARE 2017, VOL. 2 | 2018年 / 662卷
关键词
Business application; Hadoop; MPI; MapReduce; Matrix multiplication; MAPREDUCE;
D O I
10.1007/978-3-319-67621-0_3
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Business applications, such as market shops, use matrix multiplication to calculate yearly, monthly, or even daily profits based on price and quantity matrices. Matrices comprise large data in computer applications and other fields, which make the efficiency of matrix multiplication a popular research topic. Although the task of computing matrix products is a central operation in many numerical algorithms, it is potentially time consuming, making it one of the most well-studied problems in this field. In this paper, Message Passing Interface (MPI), MapReduce, and Multithreaded methods have been implemented to demonstrate their effectiveness in expediting matrix multiplication in a multi-core system. Simulation results show that the efficiency rates of MPI and MapReduce are 90.11% and 47.94%, respectively, with a multi-core processor on the Market Shop application, indicating better performances compared with those of the multithreaded and sequential methods.
引用
收藏
页码:24 / 36
页数:13
相关论文
共 50 条
  • [31] Evaluation of the performance of parallel sparse-matrix multiplication and the effect of dynamic load-balancing
    Nanri, Takeshi
    Soga, Takeshi
    Kurihara, Koji
    Gu, Feng Long
    Ishihata, Hiroaki
    Murakami, Kazuaki
    COMPUTATION IN MODERN SCIENCE AND ENGINEERING VOL 2, PTS A AND B, 2007, 2 : 106 - +
  • [32] Matrix Multiplication of Big Data Using MapReduce: A Review
    Qasem, Mais Haj
    Abu Sarhan, Alaa
    Qaddoura, Raneem
    Mahafzah, Basel A.
    PROCEEDINGS OF 2017 2ND INTERNATIONAL CONFERENCE ON THE APPLICATIONS OF INFORMATION TECHNOLOGY IN DEVELOPING RENEWABLE ENERGY PROCESSES & SYSTEMS (IT-DREPS 2017), 2017,
  • [33] Secure Strassen-Winograd Matrix Multiplication with MapReduce
    Ciucanu, Radu
    Giraud, Matthieu
    Lafourcade, Pascal
    Ye, Lihua
    PROCEEDINGS OF THE 16TH INTERNATIONAL JOINT CONFERENCE ON E-BUSINESS AND TELECOMMUNICATIONS, VOL 2: SECRYPT, 2019, : 220 - 227
  • [34] A parallel algorithm for matrix multiplication of compressed Z order data structures
    Scott, K
    PROCEEDINGS OF THE ISCA 20TH INTERNATIONAL CONFERENCE ON COMPUTERS AND THEIR APPLICATIONS, 2005, : 453 - 458
  • [35] Combining building blocks for parallel multi-level matrix multiplication
    Hunold, S.
    Rauber, T.
    Ruenger, G.
    PARALLEL COMPUTING, 2008, 34 (6-8) : 411 - 426
  • [36] Parallel matrix multiplication on a linear array with a reconfigurable pipelined bus system
    Li, KQ
    Pan, VY
    IEEE TRANSACTIONS ON COMPUTERS, 2001, 50 (05) : 519 - 525
  • [37] A Parallel matrix multiplication algorithm for some Cauchy-like matrices
    Zhang, Jieyuan
    Li, Shengguo
    Cheng, Lizhi
    2014 IEEE INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING, COMMUNICATIONS AND COMPUTING (ICSPCC), 2014, : 796 - 799
  • [38] An Efficient Parallel Divide-and-Conquer Algorithm for Generalized Matrix Multiplication
    Eagan, John
    Herdman, Marc
    Vaughn, Christian
    Bean, Nathaniel
    Kern, Sarah
    Pirouz, Matin
    2023 IEEE 13TH ANNUAL COMPUTING AND COMMUNICATION WORKSHOP AND CONFERENCE, CCWC, 2023, : 442 - 449
  • [39] Parallel sparse matrix-matrix multiplication: a scalable solution with 1D algorithm
    Hoque, Mohammad Asadul
    Raju, Md Rezaul Karim
    Tymczak, Christopher John
    Vrinceanu, Daniel
    Chilakamarri, Kiran
    INTERNATIONAL JOURNAL OF COMPUTATIONAL SCIENCE AND ENGINEERING, 2015, 11 (04) : 391 - 401
  • [40] The Strategies of Matrix Allocation and Efficient Analysis on Parallel Algorithm of Matrix Multiplication in multiple processors system
    Liu, Jun
    Chen, Li
    THIRD INTERNATIONAL SYMPOSIUM ON COMPUTER SCIENCE AND COMPUTATIONAL TECHNOLOGY (ISCSCT 2010), 2010, : 137 - 139