BAGUA: Scaling up Distributed Learning with System Relaxations

• URL: http://arxiv.org/abs/2107.01499v2
• Date: Tue, 6 Jul 2021 08:18:02 GMT
• Title: BAGUA: Scaling up Distributed Learning with System Relaxations
• Authors: Shaoduo Gan, Xiangru Lian, Rui Wang, Jianbin Chang, Chengjun Liu, Hongmei Shi, Shengzhuo Zhang, Xianghong Li, Tengxu Sun, Jiawei Jiang, Binhang Yuan, Sen Yang, Ji Liu, Ce Zhang
• Abstract summary: BAGUA is a new communication framework for distributed data-parallel training. Powered by the new system design, BAGUA has a great ability to implement and extend various state-of-the-art distributed learning algorithms. In a production cluster with up to 16 machines, BAGUA can outperform PyTorch-DDP, Horovod and BytePS in the end-to-end training time.
• Score: 31.500494636704598
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Recent years have witnessed a growing list of systems for distributed data-parallel training. Existing systems largely fit into two paradigms, i.e., parameter server and MPI-style collective operations. On the algorithmic side, researchers have proposed a wide range of techniques to lower the communication via system relaxations: quantization, decentralization, and communication delay. However, most, if not all, existing systems only rely on standard synchronous and asynchronous stochastic gradient (SG) based optimization, therefore, cannot take advantage of all possible optimizations that the machine learning community has been developing recently. Given this emerging gap between the current landscapes of systems and theory, we build BAGUA, a communication framework whose design goal is to provide a system abstraction that is both flexible and modular to support state-of-the-art system relaxation techniques of distributed training. Powered by the new system design, BAGUA has a great ability to implement and extend various state-of-the-art distributed learning algorithms. In a production cluster with up to 16 machines (128 GPUs), BAGUA can outperform PyTorch-DDP, Horovod and BytePS in the end-to-end training time by a significant margin (up to 1.95 times) across a diverse range of tasks. Moreover, we conduct a rigorous tradeoff exploration showing that different algorithms and system relaxations achieve the best performance over different network conditions.

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