title={PowerAI DDL},
  author={Minsik Cho and Ulrich Finkler and Sameer Kumar and David S. Kung and Vaibhav Saxena and Dheeraj Sreedhar},
As deep neural networks become more complex and input data-sets grow larger, it can take days or even weeks to train a deep neural network to the desired accuracy. Therefore, distributed Deep Learning at a massive scale is a critical capability, since it offers the potential to reduce the training time from weeks to hours. In this paper, we present a software-hardware co-optimized distributed Deep Learning system that can achieve near-linear scaling up to hundreds of GPUs. The core algorithm is… CONTINUE READING
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