ASYNC: A Cloud Engine with Asynchrony and History for Distributed Machine Learning
ASYNC: A Cloud Engine with Asynchrony and History for Distributed Machine Learning
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DOI：
10.1109/ipdps47924.2020.00052
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发表时间：
2019-07
期刊：
影响因子：
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通讯作者：
Saeed Soori;Bugra Can;M. Gürbüzbalaban;M. Dehnavi
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文献类型：
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作者：
Saeed Soori;Bugra Can;M. Gürbüzbalaban;M. Dehnavi
ASYNC 是一个支持在分布式计算平台上实现优化方法的异步和历史的框架。异步优化方法在分布式机器学习中越来越受欢迎。然而，它们在分布式系统上的适用性和实际实验受到限制，因为当前的批量处理云引擎没有为异步和历史提供强大的支持。通过引入三个主要模块以及簿记系统特定和应用参数，ASYNC 为从业者提供了实现异步机器学习方法的框架。为了演示 ASYNC 的易于实现性，在 ASYNC 中演示了两种著名优化方法（随机梯度下降和 SAGA）的同步和异步变体。
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.