The bulk synchronous parallel (BSP) is a celebrated synchronization model for general-purpose parallel computing that has successfully been employed for distributed training of machine learning models. A prevalent shortcoming of the BSP is that it requires workers to wait for the straggler at every iteration. To ameliorate this shortcoming of classic BSP, we propose ELASTICBSP a model that aims to relax its strict synchronization requirement. The proposed model offers more flexibility and adaptability during the training phase, without sacrificing on the accuracy of the trained model. We also propose an efficient method that materializes the model, named ZIPLINE. The algorithm is tunable and can effectively balance the trade-off between quality of convergence and iteration throughput, in order to accommodate different environments or applications. A thorough experimental evaluation demonstrates that our proposed ELASTICBSP model converges faster and to a higher accuracy than the classic BSP. It also achieves comparable (if not higher) accuracy than the other sensible synchronization models.
@article{arxiv.2001.01347,
title = {Elastic Bulk Synchronous Parallel Model for Distributed Deep Learning},
author = {Xing Zhao and Manos Papagelis and Aijun An and Bao Xin Chen and Junfeng Liu and Yonggang Hu},
journal= {arXiv preprint arXiv:2001.01347},
year = {2020}
}
Comments
The paper was accepted in the proceedings of the IEEE International Conference on Data Mining 2019 (ICDM'19), 1504-1509