Workload-aware Automatic Parallelization for Multi-GPU DNN Training
Department of Electrical and Computer Engineering, Seoul National University, Seoul, 08826 Korea
arXiv:1811.01532 [cs.DC], (5 Nov 2018)
@article{shin2018worklad,
title={Workload-aware Automatic Parallelization for Multi-GPU DNN Training},
author={Shin, Sungho and Jo, Youngmin and Choi, Jungwook and Venkataramani, Swagath and Srinivasan, Vijayalakshmi and Sung Wonyong},
year={2018},
month={nov},
archivePrefix={"arXiv"},
primaryClass={cs.DC}
}
Deep neural networks (DNNs) have emerged as successful solutions for variety of artificial intelligence applications, but their very large and deep models impose high computational requirements during training. Multi-GPU parallelization is a popular option to accelerate demanding computations in DNN training, but most state-of-the-art multi-GPU deep learning frameworks not only require users to have an in-depth understanding of the implementation of the frameworks themselves, but also apply parallelization in a straight-forward way without optimizing GPU utilization. In this work, we propose a workload-aware auto-parallelization framework (WAP) for DNN training, where the work is automatically distributed to multiple GPUs based on the workload characteristics. We evaluate WAP using TensorFlow with popular DNN benchmarks (AlexNet and VGG-16), and show competitive training throughput compared with the state-of-the-art frameworks, and also demonstrate that WAP automatically optimizes GPU assignment based on the workload’s compute requirements, thereby improving energy efficiency.
November 11, 2018 by hgpu
