Knowledge Commons of Institute of Automation,CAS
Self-Adaptive Task Allocation for Decentralized Deep Learning in Heterogeneous Environments | |
Chao, Yongyue; Liao, Mingxue; Gao, Jiaxin; Li,Guangyao | |
2022-08-17 | |
会议名称 | International Conference on Software Engineering and Knowledge Engineering |
会议录名称 | Proceedings of the International Conference on Software Engineering and Knowledge Engineering, SEKE |
页码 | 154-159 |
会议日期 | 2022-7-1至20227-10 |
会议地点 | 线上会议 |
摘要 | The demand for large-scale deep learning is increasing, and distributed training is the current mainstream solution. Decentralized algorithms are widely used in distributed training. However, in a heterogeneous environment, each worker computes the same amount of training data resulting in a lot of wasted time for waiting the straggler. In this paper, we proposed a self-adaptive task allocation algorithm (SATA) which allows that each worker acquires the amount of training data adaptively based on the performance of workers in the heterogeneous environment. In order to show the applicability of SATA in heterogeneous clusters better, we set up the heterogeneous cluster composed of two or three different types of GPUs. Besides, we conduct a series of experiments to show the performance of SATA. The experimental results illustrate several advantages of SATA. SATA can accelerate distributed training about 3.3X that of All-Reduce as well as about 1.9X-3.8X that of AD-PSGD algorithm. And the total training time of SATA is reduced from 20 to 40 percentage compared to All-Reduce. © 2022 Knowledge Systems Institute Graduate School. All rights reserved. |
收录类别 | EI |
七大方向——子方向分类 | 机器学习 |
国重实验室规划方向分类 | 实体人工智能系统(软、硬件) |
是否有论文关联数据集需要存交 | 否 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/52043 |
专题 | 复杂系统认知与决策实验室 |
通讯作者 | Chao, Yongyue |
作者单位 | 中国科学院自动化研究所 |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Chao, Yongyue,Liao, Mingxue,Gao, Jiaxin,et al. Self-Adaptive Task Allocation for Decentralized Deep Learning in Heterogeneous Environments[C],2022:154-159. |
条目包含的文件 | 下载所有文件 | |||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
Self-Adaptive Task A(1976KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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