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KAISA: An Adaptive Second-order Optimizer Framework for Deep Neural Networks

J. Gregory Pauloski, Qi Huang, Lei Huang, Shivaram Venkataraman, Kyle Chard, Ian Foster, Zhao Zhang
University of Chicago
arXiv:2107.01739 [cs.LG], (4 Jul 2021)

@misc{pauloski2021kaisa,

   title={KAISA: An Adaptive Second-order Optimizer Framework for Deep Neural Networks},

   author={J. Gregory Pauloski and Qi Huang and Lei Huang and Shivaram Venkataraman and Kyle Chard and Ian Foster and Zhao Zhang},

   year={2021},

   eprint={2107.01739},

   archivePrefix={arXiv},

   primaryClass={cs.LG}

}

Kronecker-factored Approximate Curvature (K-FAC) has recently been shown to converge faster in deep neural network (DNN) training than stochastic gradient descent (SGD); however, K-FAC’s larger memory footprint hinders its applicability to large models. We present KAISA, a K-FAC-enabled, Adaptable, Improved, and ScAlable second-order optimizer framework that adapts the memory footprint, communication, and computation given specific models and hardware to achieve maximized performance and enhanced scalability. We quantify the tradeoffs between memory and communication cost and evaluate KAISA on large models, including ResNet-50, Mask R-CNN, U-Net, and BERT, on up to 128 NVIDIA A100 GPUs. Compared to the original optimizers, KAISA converges 18.1-36.3% faster across applications with the same global batch size. Under a fixed memory budget, KAISA converges 32.5% and 41.6% faster in ResNet-50 and BERT-Large, respectively. KAISA can balance memory and communication to achieve scaling efficiency equal to or better than the baseline optimizers.
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