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arXiv · 2002.02885

Understanding and Optimizing Packed Neural Network Training for Hyper-Parameter Tuning

Rui Liu·Sanjay Krishnan·Aaron J. Elmore·Michael J. Franklin
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Abstract

As neural networks are increasingly employed in machine learning practice, how to efficiently share limited training resources among a diverse set of model training tasks becomes a crucial issue. To achieve better utilization of the shared resources, we explore the idea of jointly training multiple neural network models on a single GPU in this paper. We realize this idea by proposing a primitive, called pack. We further present a comprehensive empirical study of pack and end-to-end experiments that suggest significant improvements for hyperparameter tuning. The results suggest: (1) packing two models can bring up to 40% performance improvement over unpacked setups for a single training step and the improvement increases when packing more models; (2) the benefit of the pack primitive largely depends on a number of factors including memory capacity, chip architecture, neural network structure, and batch size; (3) there exists a trade-off between packing and unpacking when training multiple neural network models on limited resources; (4) a pack-aware Hyperband is up to 2.7x faster than the original Hyperband, with this improvement growing as memory size increases and subsequently the density of models packed.

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Rui Liu, Sanjay Krishnan, Aaron J. Elmore, Michael J. Franklin. 2020-02-07. Understanding and Optimizing Packed Neural Network Training for Hyper-Parameter Tuning. https://arxiv.org/abs/2002.02885

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