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• W3214474508 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 $1.2times sim 1.8times$ 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." @default.
• W3214474508 created "2021-11-22" @default.
• W3214474508 creator A5005538082 @default.
• W3214474508 creator A5007519257 @default.
• W3214474508 creator A5010705747 @default.
• W3214474508 creator A5073026003 @default.
• W3214474508 creator A5085908411 @default.
• W3214474508 date "2021-11-08" @default.
• W3214474508 modified "2023-10-16" @default.
• W3214474508 title "BlueFog: Make Decentralized Algorithms Practical for Optimization and Deep Learning" @default.
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• W3214474508 doi "https://doi.org/10.48550/arxiv.2111.04287" @default.
• W3214474508 hasPublicationYear "2021" @default.
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