Cooperative Distributed GPU Power Capping for Deep Learning Clusters

The recent GPU-based clusters that handle deep learning (DL) tasks have the features of GPU device heterogeneity, a variety of deep neural network (DNN) models, and high computational complexity. Thus, the traditional power capping methods for CPU-based clusters or small-scale GPU devices cannot be...

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Bibliographic Details
Published in:IEEE transactions on industrial electronics (1982) 2022-07, Vol.69 (7), p.7244-7254
Main Authors: Kang, Dong-Ki, Ha, Yun-Gi, Peng, Limei, Youn, Chan-Hyun
Format: Article
Language:English
Subjects:
Artificial neural networks
Capping
Clusters
Completion time
Deep learning
Deep learning (DL) cluster
Economic analysis
economic model predictive control (EMPC)
Economic models
GPU power capping
Graphics processing units
Heterogeneity
Lagrangian dual decomposition
Lipschitz continuity
Machine learning
Performance evaluation
Power demand
Predictive control
Servers
Task analysis
Training
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Summary:The recent GPU-based clusters that handle deep learning (DL) tasks have the features of GPU device heterogeneity, a variety of deep neural network (DNN) models, and high computational complexity. Thus, the traditional power capping methods for CPU-based clusters or small-scale GPU devices cannot be applied to the GPU-based clusters handling DL tasks. This article develops a cooperative distributed GPU power capping (CD-GPC) system for GPU-based clusters, aiming to minimize the training completion time of invoked DL tasks without exceeding the limited power budget. Specifically, we first design the frequency scaling approach using the online model estimation based on the recursive least square method. This approach achieves the accurate tuning for DL task training time and power usage of GPU devices without needing offline profiling. Then, we formulate the proposed FS problem as a Lagrangian dual decomposition-based economic model predictive control problem for large-scale heterogeneous GPU clusters. We conduct both the NVIDIA GPU-based lab-scale real experiments and real job trace-based simulation experiments for performance evaluation. Experimental results validate that the proposed system improves the power capping accuracy to have a mean absolute error of < \!1\%, and reduces the deadline violation ratio of invoked DL tasks by 21.5% compared with other recent counterparts.
ISSN:0278-0046
1557-9948
DOI:10.1109/TIE.2021.3095790