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  • Open Access

    ARTICLE

    Research on Performance Optimization of Spark Distributed Computing Platform

    Qinlu He1,*, Fan Zhang1, Genqing Bian1, Weiqi Zhang1, Zhen Li2

    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2833-2850, 2024, DOI:10.32604/cmc.2024.046807 - 15 May 2024

    Abstract Spark, a distributed computing platform, has rapidly developed in the field of big data. Its in-memory computing feature reduces disk read overhead and shortens data processing time, making it have broad application prospects in large-scale computing applications such as machine learning and image processing. However, the performance of the Spark platform still needs to be improved. When a large number of tasks are processed simultaneously, Spark’s cache replacement mechanism cannot identify high-value data partitions, resulting in memory resources not being fully utilized and affecting the performance of the Spark platform. To address the problem that… More >

  • Open Access

    ARTICLE

    CNN Accelerator Using Proposed Diagonal Cyclic Array for Minimizing Memory Accesses

    Hyun-Wook Son1, Ali A. Al-Hamid1,2, Yong-Seok Na1, Dong-Yeong Lee1, Hyung-Won Kim1,*

    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1665-1687, 2023, DOI:10.32604/cmc.2023.038760 - 30 August 2023

    Abstract This paper presents the architecture of a Convolution Neural Network (CNN) accelerator based on a new processing element (PE) array called a diagonal cyclic array (DCA). As demonstrated, it can significantly reduce the burden of repeated memory accesses for feature data and weight parameters of the CNN models, which maximizes the data reuse rate and improve the computation speed. Furthermore, an integrated computation architecture has been implemented for the activation function, max-pooling, and activation function after convolution calculation, reducing the hardware resource. To evaluate the effectiveness of the proposed architecture, a CNN accelerator has been… More >

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