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ASAP.SGD: Instance-based Adaptiveness to Staleness in Asynchronous SGD

ICML 2022 · Optimization › Optimizers · approach: SGD momentum and step-size schedules

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Add TAIL-τ instance-based staleness-adaptive step size (mean-preserving)AsyncSGD (ASYNC, HOG, LSH) on LeNet · MNIST, Fashion-MNIST, CIFAR-10wall-clock time to convergence speedup: up to 1.92x (5%) and avg 1.29-1.6x across settingsHelped
Replace constant step size with commonly used ×τ^-1 staleness dampening (ablation)AsyncSGD on MNIST (MLP and LeNet) · MNISTconvergence rateHurt
Replace constant step size with FLeet exponential dampening (ablation)AsyncSGD on MNIST (MLP and LeNet) · MNISTconvergence rateHurt
Add TAIL-τ step size to AsyncSGD implementationsAsyncSGD (HOG, LSH, ASYNC) · MNIST and Fashion-MNIST higher-precision convergencefraction of executions reaching target precision: success ratio 3.53 for MLP MNIST 5%-convergenceHelped

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