Offline performance and energy consumption prediction model of deep learning training tasks

Jianchen Han; Yingwen Chen

doi:10.1117/12.2671695

21 March 2023 Offline performance and energy consumption prediction model of deep learning training tasks

Jianchen Han, Yingwen Chen

Proceedings Volume 12609, International Conference on Computer Application and Information Security (ICCAIS 2022); 1260915 (2023) https://doi.org/10.1117/12.2671695
Event: International Conference on Computer Application and Information Security (ICCAIS 2022), 2022, ONLINE, ONLINE

Abstract

Today, deep learning technology is widely used in a lot of scientific research fields. A crucial question is how to accurately predict the performance and energy consumption of deep learning training (DLT) tasks. Existing prediction methods of DLT tasks either have low accuracy or use too many cluster resources, and few methods focus on the energy consumption prediction. In this paper, we analyze the relationships between the characteristics of performance and energy consumption and the task configurations of DLT tasks. Then we propose an offline prediction model to predict the performance and energy consumption of DLT tasks based on these relationships. The experiment in an actual GPU cluster shows the effectiveness of the prediction model. The average deviation of the prediction model is 4.68%.

Citation Download Citation

Jianchen Han and Yingwen Chen "Offline performance and energy consumption prediction model of deep learning training tasks", Proc. SPIE 12609, International Conference on Computer Application and Information Security (ICCAIS 2022), 1260915 (21 March 2023); https://doi.org/10.1117/12.2671695

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KEYWORDS

Education and training

Performance modeling

Deep learning

Databases

Visual process modeling

Data modeling

Clouds

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