Multi-GPU Training of ConvNets
Omry Yadan, Keith Adams, Yaniv Taigman, Marc'Aurelio Ranzato
TL;DR
This paper evaluates various methods for parallelizing convolutional neural network training across multiple GPUs to improve efficiency and scalability.
Contribution
It systematically compares different multi-GPU training approaches for ConvNets, highlighting their advantages and limitations.
Findings
Certain parallelization methods significantly reduce training time.
Scalability varies depending on the approach used.
Recommendations for effective multi-GPU training are provided.
Abstract
In this work we evaluate different approaches to parallelize computation of convolutional neural networks across several GPUs.
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Taxonomy
TopicsAdvanced Neural Network Applications · Stochastic Gradient Optimization Techniques · Domain Adaptation and Few-Shot Learning