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Parallel Computing Considerations

  • Jun Zhao
  • Wei Wang
  • Chunyang Sheng
Chapter
Part of the Information Fusion and Data Science book series (IFDS)

Abstract

This chapter discusses the computational cost of machine learning model. To reduce its training time is a requisite of its industrial applications since a production process usually requires real-time responses. The commonly used method to accelerate the training process is to develop a parallel computing framework. In literature, two kinds of popular methods speeding up the training involves the one with a computer equipped with graphics processor unit (GPU) and the one with computer cluster including a number of computers. This chapter firstly introduces the basic ideas of GPU acceleration (e.g., the compute unified device architecture (CUDA) created by NVIDIA™) and the computer cluster framework (e.g., the MapReduce framework), then gives some specified examples of them. When training an EKF-based Elman network, the inversion operation of a Jacobian matrix is the most time-consuming procedure; a parallel computing strategy for such an operation is therefore proposed by using the CUDA-based GPU acceleration. Besides, with regard to the LSSVM modeling, a CUDA-based parallel PSO is then introduced for its hyper-parameters optimization. As for the computer cluster version, we design a parallelized EKF based on ESN by using MapReduce framework for acceleration. At the end, we also present a series of experimental analysis by using the practical energy data in steel industry to validate the performance of the accelerating approaches.

Keywords

Parallel computing; Large datasets GPU Acceleration CUDA cuBLAS Computer cluster Hadoop MapReduce EKF Elman networks Matrix inversion Jacobian matrix Online optimization LSSVM PSO 

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Jun Zhao
    • 1
  • Wei Wang
    • 1
  • Chunyang Sheng
    • 2
  1. 1.Dalian University of TechnologyDalianChina
  2. 2.Shandong University of Science and TechnologyQingdaoChina

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