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Utilization-prediction-aware energy optimization approach for heterogeneous GPU clusters

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Abstract

Optimizing energy consumption in heterogeneous GPU clusters is of paramount importance to enhance overall system efficiency and reduce operational costs. However, the diversity of GPU types in heterogeneous GPU clusters poses challenges for energy optimization. In this paper, we propose an utilization-prediction-aware energy optimization approach for heterogeneous GPU clusters. We utilize a feature correlation-based method to select feature vectors and improve the prediction accuracy of our GPU utilization prediction model. By constructing a model for energy consumption and combining it with the predicted results, our approach can avoid wasting too much energy on idle GPUs and reduce the energy consumption of the heterogeneous GPU cluster. To validate our approach, experiments are conducted on real Alibaba data. The results show that our model achieved a good performance in predicting the utilization for four types of GPUs, with RMSE, MAE and \(R^{2}\) average values of 7.08, 3.81 and 0.93, respectively. Moreover, we calculate the energy consumption of the GPU cluster before and after the adjustment, and estimate the energy savings, which amount to an average of 35.55%.

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Data availability

The datasets is available at: https://github.com/alibaba/clusterdata/.

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Funding

National Natural Science Foundation of China project (Nos. 61472256, 61170277), Technology Development Fund Project of Shanghai of University for Science and Technology (Nos. 16KJFZ035, 2017KJFZ033);Anhui Provincial Natural Science Foundation General Project (Nos. KJ2020B08, KJ2020B04).

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Authors and Affiliations

  1. Business School, University of Shanghai for Science and Technology, 334 Jungong Road, Shanghai, 200000, Shanghai, China

    Sheng Wang & Shiping Chen

  2. School of Mathematics and Finance, Chuzhou University, 1 HuifengRoad, Chuzhou, 239000, Anhui, China

    Sheng Wang & Yumei Shi

  3. School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, 334 Jungong Road, Shanghai, 200000, Shanghai, China

    Shiping Chen

Authors
  1. Sheng Wang
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  2. Shiping Chen
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  3. Yumei Shi
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Contributions

SW and SC contributed to the conception of the study; SW and YS performed the experiment; SW, SC and YS contributed significantly to analysis and manuscript preparation; SW, SC and YS performed the data analyses and wrote the manuscript; SW and SC helped perform the analysis with constructive discussions.

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Correspondence to Shiping Chen.

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Wang, S., Chen, S. & Shi, Y. Utilization-prediction-aware energy optimization approach for heterogeneous GPU clusters. J Supercomput 80, 9554–9578 (2024). https://doi.org/10.1007/s11227-023-05807-x

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