NeurIPS 2018spotlight138 citations
Graph Oracle Models, Lower Bounds, and Gaps for Parallel Stochastic Optimization
Blake E Woodworth, Jialei Wang, Adam Smith, Brendan McMahan, Nati Srebro
Abstract
We suggest a general oracle-based framework that captures parallel stochastic optimization in different parallelization settings described by a dependency graph, and derive generic lower bounds in terms of this graph. We then use the framework and derive lower bounds to study several specific parallel optimization settings, including delayed updates and parallel processing with intermittent communication. We highlight gaps between lower and upper bounds on the oracle complexity, and cases where the ``natural'' algorithms are not known to be optimal.
BibTeX
@inproceedings{NEURIPS2018_3ec27c2c,
author = {Woodworth, Blake E and Wang, Jialei and Smith, Adam and McMahan, Brendan and Srebro, Nati},
booktitle = {Advances in Neural Information Processing Systems},
editor = {S. Bengio and H. Wallach and H. Larochelle and K. Grauman and N. Cesa-Bianchi and R. Garnett},
pages = {},
publisher = {Curran Associates, Inc.},
title = {Graph Oracle Models, Lower Bounds, and Gaps for Parallel Stochastic Optimization},
url = {https://proceedings.neurips.cc/paper_files/paper/2018/file/3ec27c2cff04bc5fd2586ca36c62044e-Paper.pdf},
volume = {31},
year = {2018}
}