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    Huffman Coding Based Encoding Techniques for Fast Distributed Deep Learning

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    Type
    Conference Paper
    Authors
    Gajjala, Rishikesh R.
    Banchhor, Shashwat
    Abdelmoniem, Ahmed M.
    Dutta, Aritra
    Canini, Marco cc
    Kalnis, Panos cc
    KAUST Department
    KAUST
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Computer Science Program
    Date
    2020-12
    Permanent link to this record
    http://hdl.handle.net/10754/666175
    
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    Abstract
    Distributed stochastic algorithms, equipped with gradient compression techniques, such as codebook quantization, are becoming increasingly popular and considered state-of-the-art in training large deep neural network (DNN) models. However, communicating the quantized gradients in a network requires efficient encoding techniques. For this, practitioners generally use Elias encoding-based techniques without considering their computational overhead or data-volume. In this paper, based on Huffman coding, we propose several lossless encoding techniques that exploit different characteristics of the quantized gradients during distributed DNN training. Then, we show their effectiveness on 5 different DNN models across three different data-sets, and compare them with classic state-of-the-art Elias-based encoding techniques. Our results show that the proposed Huffman-based encoders (i.e., RLH, SH, and SHS) can reduce the encoded data-volume by up to 5.1×, 4.32×, and 3.8×, respectively, compared to the Elias-based encoders.
    Citation
    Gajjala, R. R., Banchhor, S., Abdelmoniem, A. M., Dutta, A., Canini, M., & Kalnis, P. (2020). Huffman Coding Based Encoding Techniques for Fast Distributed Deep Learning. Proceedings of the 1st Workshop on Distributed Machine Learning. doi:10.1145/3426745.3431334
    Publisher
    ACM
    ISBN
    9781450381826
    DOI
    10.1145/3426745.3431334
    Additional Links
    https://dl.acm.org/doi/10.1145/3426745.3431334
    ae974a485f413a2113503eed53cd6c53
    10.1145/3426745.3431334
    Scopus Count
    Collections
    Conference Papers; Computer Science Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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