  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
improve performance and increase 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. KAISA is open
source and available at https://github.com/gpauloski/kfac_pytorch.
