Throughput Prediction of Asynchronous SGD in TensorFlow
Zhuojin Li,
Wumo Yan,
Marco Paolieri
et al.
Abstract:Modern machine learning frameworks can train neural networks using multiple nodes in parallel, each computing parameter updates with stochastic gradient descent (SGD) and sharing them asynchronously through a central parameter server. Due to communication overhead and bottlenecks, the total throughput of SGD updates in a cluster scales sublinearly, saturating as the the number of nodes increases. In this paper, we present a solution to predicting training throughput from profiling traces collected from a singl… Show more
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