ASAGA: Asynchronous Parallel SAGA

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Authors Simon Lacoste-Julien, RĂ©mi Leblond, Fabian Pedregosa
Journal/Conference Name Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, AISTATS 2017
Paper Category
Paper Abstract We describe ASAGA, an asynchronous parallel version of the incremental gradient algorithm SAGA that enjoys fast linear convergence rates. Through a novel perspective, we revisit and clarify a subtle but important technical issue present in a large fraction of the recent convergence rate proofs for asynchronous parallel optimization algorithms, and propose a simplification of the recently introduced "perturbed iterate" framework that resolves it. We thereby prove that ASAGA can obtain a theoretical linear speedup on multi-core systems even without sparsity assumptions. We present results of an implementation on a 40-core architecture illustrating the practical speedup as well as the hardware overhead.
Date of publication 2016
Code Programming Language Scala
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