Squeeze more juice out of a single GPU in deep learning

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  Date: November 15, 12:00pm-1:00pm ET

   

  Presenters: Weiguang Guan (SHARCNET)

   

  It’s well known that GPUs can significantly accelerate neural network training. However, not everyone knows that a single GPU is sufficient to train most neural networks except for a few large ones (like LLMs). In fact, a GPU is under-utilized in most cases. In this talk, we are addressing the under-utilization issue and proposing a way to make full use of the GPU capacity. The goal is to increase the throughput with a single GPU. We will use a small NN training as an example to illustrate how to achieve the goal by splitting a physical GPU into multiple logical GPUs and then running a particular training process per logical GPU.

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    This event is part of the Compute Ontario Colloquium Series.