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

Saeed Soori, Bugra Can, Mert Gurbuzbalaba, Maryam Mehri Dehnavi
2020 arXiv   pre-print
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
more » ... pecific 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.
arXiv:1907.08526v4 fatcat:uhwcdnl67bcczdncxgrv6hod7a