GPARS: Graph predictive algorithm for efficient resource scheduling in heterogeneous GPU clusters
Efficient resource scheduling in heterogeneous graphics processing unit (GPU) clusters are critical for maximizing system performance and optimizing resource utilization. However, prior research in resource scheduling algorithms typically employed machine learning (ML) algorithms to estimate job dur...
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| Vydané v: | Future generation computer systems Ročník 152; s. 127 - 137 |
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| Jazyk: | English |
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Elsevier B.V
01.03.2024
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| ISSN: | 0167-739X, 1872-7115 |
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| Abstract | Efficient resource scheduling in heterogeneous graphics processing unit (GPU) clusters are critical for maximizing system performance and optimizing resource utilization. However, prior research in resource scheduling algorithms typically employed machine learning (ML) algorithms to estimate job durations or GPU utilization in the cluster based on training progress and task speed. Regrettably, these studies often overlooked the performance variations among different GPU types within these clusters, as well as the presence of spatiotemporal correlations among jobs. To address these limitations, this paper introduces the graph predictive algorithm for efficient resource scheduling (GPARS) designed specifically for heterogeneous clusters. GPARS leverages spatiotemporal correlations among jobs and utilizes graph attention networks (GANs) for precise job duration prediction. Building upon the prediction results, we develop a dynamic objective function to allocate suitable GPU types for newly submitted jobs. By conducting a comprehensive analysis of Alibaba’s heterogeneous GPU cluster, we delve into the impact of GPU capacity and type on job completion time (JCT) and resource utilization. Our evaluation, using real traces from Alibaba and Philly, substantiates the effectiveness of GPARS. It achieves a remarkable 10.29% reduction in waiting time and an average improvement of 7.47% in resource utilization compared to the original scheduling method. These findings underscore GPARS’s superior performance in enhancing resource scheduling within heterogeneous GPU clusters.
•After analyzing Alibaba’s GPU cluster, we study GPU capacity and type’s impact on job completion time and resource use. Our findings highlight overcrowding in weaker GPUs and load imbalance in high-end machines.•Our GAN approach outperforms, achieving impressive RMSE and MAE: 0.0237 and 0.0073. Our study confirms spatiotemporal correlations in job durations within the cluster.•Leveraging our predictions, we introduce GPARS, an efficient scheduling approach for job-GPU allocation. Evaluated with Alibaba and Philly traces, GPARS substantially cuts waiting time by 10.29 |
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| AbstractList | Efficient resource scheduling in heterogeneous graphics processing unit (GPU) clusters are critical for maximizing system performance and optimizing resource utilization. However, prior research in resource scheduling algorithms typically employed machine learning (ML) algorithms to estimate job durations or GPU utilization in the cluster based on training progress and task speed. Regrettably, these studies often overlooked the performance variations among different GPU types within these clusters, as well as the presence of spatiotemporal correlations among jobs. To address these limitations, this paper introduces the graph predictive algorithm for efficient resource scheduling (GPARS) designed specifically for heterogeneous clusters. GPARS leverages spatiotemporal correlations among jobs and utilizes graph attention networks (GANs) for precise job duration prediction. Building upon the prediction results, we develop a dynamic objective function to allocate suitable GPU types for newly submitted jobs. By conducting a comprehensive analysis of Alibaba’s heterogeneous GPU cluster, we delve into the impact of GPU capacity and type on job completion time (JCT) and resource utilization. Our evaluation, using real traces from Alibaba and Philly, substantiates the effectiveness of GPARS. It achieves a remarkable 10.29% reduction in waiting time and an average improvement of 7.47% in resource utilization compared to the original scheduling method. These findings underscore GPARS’s superior performance in enhancing resource scheduling within heterogeneous GPU clusters.
•After analyzing Alibaba’s GPU cluster, we study GPU capacity and type’s impact on job completion time and resource use. Our findings highlight overcrowding in weaker GPUs and load imbalance in high-end machines.•Our GAN approach outperforms, achieving impressive RMSE and MAE: 0.0237 and 0.0073. Our study confirms spatiotemporal correlations in job durations within the cluster.•Leveraging our predictions, we introduce GPARS, an efficient scheduling approach for job-GPU allocation. Evaluated with Alibaba and Philly traces, GPARS substantially cuts waiting time by 10.29 |
| Author | Chen, Shiping Wang, Sheng Shi, Yumei |
| Author_xml | – sequence: 1 givenname: Sheng surname: Wang fullname: Wang, Sheng organization: Business School, University of Shanghai for Science and Technology, Shanghai 200082, PR China – sequence: 2 givenname: Shiping surname: Chen fullname: Chen, Shiping email: chensp@usst.edu.cn organization: Business School, University of Shanghai for Science and Technology, Shanghai 200082, PR China – sequence: 3 givenname: Yumei surname: Shi fullname: Shi, Yumei organization: School of Mathematics and Finance, Chuzhou University, Chuzhou 239000, Anhui, PR China |
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| Cites_doi | 10.1145/3190508.3190517 10.1109/TPDS.2021.3079202 10.1145/2628071.2628117 10.1016/j.future.2023.07.011 10.1145/3575693.3575705 10.1016/j.jnca.2013.10.009 10.1145/2829988.2787488 10.1145/2647868.2654889 10.1016/j.knosys.2023.110609 10.1016/j.ipm.2023.103328 10.1016/j.ipm.2022.103137 10.1145/1272996.1273005 10.1016/j.eswa.2022.118790 10.1145/1998582.1998637 10.1109/TSE.2023.3285280 10.1109/TNNLS.2020.2978386 10.1145/2168836.2168847 10.1145/3342195.3387555 10.1145/3458817.3476223 10.1109/TPDS.2021.3136245 10.21203/rs.3.rs-2266264/v1 10.1016/j.future.2023.03.041 10.1016/j.future.2023.05.032 10.1145/2523616.2523633 10.1145/3292500.3330925 10.1016/j.compmedimag.2021.102027 10.1016/j.jpdc.2020.05.014 |
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| Keywords | Graph attention networks Resource scheduling Resource utilization Heterogeneous GPU clusters Job duration Waiting time |
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