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Enhancing Distributed Neural Network Training Through Node-Based Communications.

Sergio Moreno-Alvarez, Mercedes E Paoletti, Gabriele Cavallaro

    IEEE Transactions on Neural Networks and Learning Systems
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    This study introduces node-based optimizations to reduce gradient communication in deep neural networks (DNNs), significantly cutting training time and improving accuracy in distributed settings.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Modern deep neural networks (DNNs) require vast amounts of data, leading to substantial computational demands.
    • Data parallelism strategies, while improving runtimes, face bottlenecks due to extensive gradient communication.
    • Communication latency in distributed platforms significantly impacts the efficiency of DNN training.

    Purpose of the Study:

    • To develop and present node-based optimization steps for reducing gradient exchange in DNNs.
    • To create a versatile communication scheme applicable to various general-purpose DNN algorithms.
    • To address communication latency issues hindering performance on distributed platforms.

    Main Methods:

    • Implemented node-based optimization to minimize gradient exchange between model replicas.
    • Developed a communication scheme considering the location of each replica within the distributed platform.
    • Evaluated the proposal using diverse neural network architectures, datasets, and application types.

    Main Results:

    • Demonstrated significant reduction in global training time for deep neural networks.
    • Achieved a slight improvement in model accuracy.
    • Validated the robustness and versatility of the proposed communication scheme across different scenarios.

    Conclusions:

    • The proposed node-based optimization effectively reduces gradient communication bottlenecks in DNN training.
    • The versatile communication scheme enhances efficiency and accuracy in distributed deep learning.
    • This approach offers a practical solution for improving the performance of large-scale DNNs.