The computational requirements of deep neural networks used to enable AI applications like self-driving cars are enormous. A single training cycle can take weeks on a single GPU, or even years for the larger datasets like those used in self-driving car research. Using multiple GPUs for deep learning can significantly shorten the time required to train lots of data, making solving complex problems with deep learning feasible.

• Approaches to multi-GPU training

• Algorithmic and engineering challenges to large-scale trainin

• Key techniques used to overcome the challenges mentioned above

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