《信息与软件技术》是一本国际档案期刊,主要关注有助于改进软件开发实践的研究和经验。该杂志的范围包括更好地设计软件和管理其开发的方法和技术。提交审查的文章应该有一个明确的软件工程的组成部分,或者说明如何改进软件开发的工程和管理。官网地址: http://dblp.uni-trier.de/db/journals/infsof/index.html

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Distributed machine learning (ML) can bring more computational resources to bear than single-machine learning, reducing training time. Further, distribution allows models to be partitioned over many machines, allowing very large models to be trained -- models that may be much larger than the available memory of any individual machine. However, in practice, distributed ML remains challenging, primarily due to high communication costs. We propose a new approach to distributed neural network learning, called independent subnet training (IST). In IST, a neural network is decomposed into a set of subnetworks of the same depth as the original network, each of which is trained locally, before the various subnets are exchanged and the process is repeated. IST training has many advantages over standard data parallel approaches. Because the subsets are independent, communication frequency is reduced. Because the original network is decomposed into independent parts, communication volume is reduced. Further, the decomposition makes IST naturally model parallel, and so IST scales to very large models that cannot fit on any single machine. We show experimentally that IST results in training time that are much lower than data parallel approaches to distributed learning, and that it scales to large models that cannot be learned using standard approaches.

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