Cluster Computing

, Volume 22, Issue 3, pp 965–979 | Cite as

Event driven power consumption optimization control model of GPU clusters

  • Haifeng WangEmail author
  • Yunpeng Cao


Reducing power consumption for GPU cluster in large-scale stream computing process can bring various benefits such as reducing operating costs and environmental effect. We formulate the problem of power consumption as a constrained optimization problem, minimizing power state of cluster nodes to reduce power consumption while guaranteeing system performance and reliability. The proposed control model based on Model Prediction Control is designed to make a comprehensive metric of GPU cluster achieve expected performance, energy efficiency and reliability. It is different from the previous models, which just consider power consumption as the sole control objective. The event-triggering mechanism is introduced to reduce control overhead. It successfully separates sampling cluster status signals from control model. So the controller needs not to periodically interrupt computing process to solve optimal solutions. Finally, we evaluate and compare this control model with the previous control model by using artificial and real-world workloads. The experimental results show that our proposed control model is able to outperform existing techniques.


Energy conservation GPU cluster Power consumption control Model predictive control Stream computing 



This work was supported by the National Nature Science Foundation of China (No. 60970012), Shandong Provincial Natural Science Foundation, China (No. ZR2017MF050), Project of Shandong Province Higher Educational Science and technology program (No. J17KA049) and Shandong Province Key Research and Development Program of China (No. 2018GGX101005, 2017CXGC0701, 2016GGX109001).


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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Information Science and Engineer SchoolLin Yi UniversityLinyiChina

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