# Energy-Efficient GPU Clusters Scheduling for Deep Learning

arXiv: https://arxiv.org/abs/2304.06381 · HTML: https://mlsanity.com/paper/2304.06381/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Apr 2023.

## Built on by (most cited first, 3 of 3)

- [Offline Energy-Optimal LLM Serving: Workload-Based Energy Models for LLM Inference on Heterogeneous Systems](https://mlsanity.com/paper/2407.04014/) (2024)
- [Hybrid Heterogeneous Clusters Can Lower the Energy Consumption of LLM Inference Workloads](https://mlsanity.com/paper/2407.00010/) (2024)
- [PolyThrottle: Energy-efficient Neural Network Inference on Edge Devices](https://mlsanity.com/paper/2310.19991/) (2023)

## Abstract (arXiv)

Training deep neural networks (DNNs) is a major workload in datacenters today, resulting in a tremendously fast growth of energy consumption. It is important to reduce the energy consumption while completing the DL training jobs early in data centers. In this paper, we propose PowerFlow, a GPU clusters scheduler that reduces the average Job Completion Time (JCT) under an energy budget. We first present performance models for DL training jobs to predict the throughput and energy consumption performance with different configurations. Based on the performance models, PowerFlow dynamically allocates GPUs and adjusts the GPU-level or job-level configurations of DL training jobs. PowerFlow applies network packing and buddy allocation to job placement, thus avoiding extra energy consumed by cluster fragmentations. Evaluation results show that under the same energy consumption, PowerFlow improves the average JCT by 1.57 - 3.39 x at most, compared to competitive baselines.