BlueFog: Make Decentralized Algorithms Practical for Optimization and Deep Learning

• URL: http://arxiv.org/abs/2111.04287v1
• Date: Mon, 8 Nov 2021 06:06:39 GMT
• Title: BlueFog: Make Decentralized Algorithms Practical for Optimization and Deep Learning
• Authors: Bicheng Ying, Kun Yuan, Hanbin Hu, Yiming Chen, Wotao Yin
• Abstract summary: We introduce BlueFog, a python library for straightforward, high-performance implementations of decentralized algorithms. Based on a unified abstraction of various communication operations, BlueFog offers intuitive interfaces to implement a spectrum of decentralized algorithms. BlueFog reaches a much higher throughput and achieves an overall $1.2times sim 1.8times$ speedup over Horovod, a state-of-the-art distributed deep learning package.
• Score: 29.427785235669358
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Decentralized algorithm is a form of computation that achieves a global goal through local dynamics that relies on low-cost communication between directly-connected agents. On large-scale optimization tasks involving distributed datasets, decentralized algorithms have shown strong, sometimes superior, performance over distributed algorithms with a central node. Recently, developing decentralized algorithms for deep learning has attracted great attention. They are considered as low-communication-overhead alternatives to those using a parameter server or the Ring-Allreduce protocol. However, the lack of an easy-to-use and efficient software package has kept most decentralized algorithms merely on paper. To fill the gap, we introduce BlueFog, a python library for straightforward, high-performance implementations of diverse decentralized algorithms. Based on a unified abstraction of various communication operations, BlueFog offers intuitive interfaces to implement a spectrum of decentralized algorithms, from those using a static, undirected graph for synchronous operations to those using dynamic and directed graphs for asynchronous operations. BlueFog also adopts several system-level acceleration techniques to further optimize the performance on the deep learning tasks. On mainstream DNN training tasks, BlueFog reaches a much higher throughput and achieves an overall $1.2\times \sim 1.8\times$ speedup over Horovod, a state-of-the-art distributed deep learning package based on Ring-Allreduce. BlueFog is open source at https://github.com/Bluefog-Lib/bluefog.

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