ASYNC: A Cloud Engine with Asynchrony and History for Distributed Machine Learning

ASYNC is a framework that supports the implementation of asynchrony and history for optimization methods on distributed computing platforms. The popularity of asynchronous optimization methods has increased in distributed machine learning. However, their applicability and practical experimentation o...

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Bibliographic Details
Published in:arXiv.org 2020-02
Main Authors: Soori, Saeed, Can, Bugra, Gurbuzbalaba, Mert, Maryam Mehri Dehnavi
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
Computer networks
Distributed processing
Experimentation
Machine learning
Optimization
Platforms
Online Access:Get full text
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Summary:ASYNC is a framework that supports the implementation of asynchrony and history for optimization methods on distributed computing platforms. The popularity of asynchronous optimization methods has increased in distributed machine learning. However, their applicability and practical experimentation on distributed systems are limited because current bulk-processing cloud engines do not provide a robust support for asynchrony and history. With introducing three main modules and bookkeeping system-specific and application parameters, ASYNC provides practitioners with a framework to implement asynchronous machine learning methods. To demonstrate ease-of-implementation in ASYNC, the synchronous and asynchronous variants of two well-known optimization methods, stochastic gradient descent and SAGA, are demonstrated in ASYNC.
ISSN:2331-8422