Slow and Stale Gradients Can Win the Race

نویسندگان

چکیده

Distributed Stochastic Gradient Descent (SGD) when run in a synchronous manner, suffers from delays runtime as it waits for the slowest workers (stragglers). Asynchronous methods can alleviate stragglers, but cause gradient staleness that adversely affect convergence xmlns:xlink="http://www.w3.org/1999/xlink">error . In this work, we present novel theoretical characterization of speedup offered by asynchronous analyzing trade-off between trained model and actual training (wallclock time). The main novelty our work is runtime analysis considers random straggling delays, which helps us design compare distributed SGD algorithms strike balance staleness. We also provide new error variants without bounded or exponential delay assumptions. Finally, based on error-runtime trade-off, propose method gradually varying synchronicity demonstrate its performance CIFAR10 dataset.

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ژورنال

عنوان ژورنال: IEEE journal on selected areas in information theory

سال: 2021

ISSN: ['2641-8770']

DOI: https://doi.org/10.1109/jsait.2021.3103770