EaCO: Resource Sharing Dynamics and Its Impact on Energy Efficiency for DNN Training
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arXiv
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| Format: | Preprint |
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2024
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| author | Haghshenas, Kawsar Hashemi, Mona |
| author_facet | Haghshenas, Kawsar Hashemi, Mona |
| contents | Deep Learning Training (DLT) is a growing workload in shared GPU/CPU clusters due to its high computational cost and increasing number of jobs. This contributes to significant energy consumption in GPU clusters, further exacerbated by GPU under-utilization, as shown in production cluster logs. Addressing this challenge requires workload scheduling and resource allocation policies for efficient GPU sharing to improve resource and energy efficiency while maintaining performance. However, previous works primarily optimize for performance, often overlooking or even sacrificing energy efficiency.
In this paper, we present EaCO, the first energy-aware scheduling algorithm designed specifically for DLT workloads in GPU clusters. EaCO leverages hardware-supported context switching to enable GPU sharing across multiple DLT jobs, improving resource and energy utilization. GPU sharing can increase Job Completion Time (JCT) and may lead to contention if not employed carefully. To address this, EaCO integrates experiment and historical-based predictions as well as early-stage observations, ensuring performance expectations are met while optimizing energy efficiency.
We begin by experimentally exploring the dynamics of co-locating DLTs, investigating its impact on energy and resource utilization. Our results show that co-location improves energy efficiency by up to 44% for individual jobs, and increases average GPU utilization to as high as 97%. Additionally, evaluations on large-scale clusters using production traces demonstrate that EaCO reduces total energy by up to 39% compared to existing algorithms, which comes with a minimal increase in job runtime-less than 3.2% in our simulations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_08294 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | EaCO: Resource Sharing Dynamics and Its Impact on Energy Efficiency for DNN Training Haghshenas, Kawsar Hashemi, Mona Distributed, Parallel, and Cluster Computing Deep Learning Training (DLT) is a growing workload in shared GPU/CPU clusters due to its high computational cost and increasing number of jobs. This contributes to significant energy consumption in GPU clusters, further exacerbated by GPU under-utilization, as shown in production cluster logs. Addressing this challenge requires workload scheduling and resource allocation policies for efficient GPU sharing to improve resource and energy efficiency while maintaining performance. However, previous works primarily optimize for performance, often overlooking or even sacrificing energy efficiency. In this paper, we present EaCO, the first energy-aware scheduling algorithm designed specifically for DLT workloads in GPU clusters. EaCO leverages hardware-supported context switching to enable GPU sharing across multiple DLT jobs, improving resource and energy utilization. GPU sharing can increase Job Completion Time (JCT) and may lead to contention if not employed carefully. To address this, EaCO integrates experiment and historical-based predictions as well as early-stage observations, ensuring performance expectations are met while optimizing energy efficiency. We begin by experimentally exploring the dynamics of co-locating DLTs, investigating its impact on energy and resource utilization. Our results show that co-location improves energy efficiency by up to 44% for individual jobs, and increases average GPU utilization to as high as 97%. Additionally, evaluations on large-scale clusters using production traces demonstrate that EaCO reduces total energy by up to 39% compared to existing algorithms, which comes with a minimal increase in job runtime-less than 3.2% in our simulations. |
| title | EaCO: Resource Sharing Dynamics and Its Impact on Energy Efficiency for DNN Training |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2412.08294 |