CO<sub>2</sub> Emission Aware Scheduling for Deep Neural Network Training Workloads

Haghshenas, Kawsar; Setz, Brian; Aiello, Marco

doi:10.1109/bigdata55660.2022.10020544

Cited by 2 publications

(1 citation statement)

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“…Carbon-aware job scheduling utilizes the variation of carbon intensity based on time (Li et al, 2016;Haghshenas et al, 2022) and location (Moghaddam, 2014;Berl et al, 2009) in order to reduce the carbon emissions of DNN training. But due to various constraints such as large datasets (Caesar et al, 2019;Dai et al, 2017;Deng et al, 2009), data regulations (GDPR, 2018), and availability of resources, moving jobs to greener geographical locations is not always viable.…”

Section: Related Workmentioning

confidence: 99%

Chasing Low-Carbon Electricity for Practical and Sustainable DNN Training

Yang¹,

Meng²,

Chung³

et al. 2023

Preprint

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Deep learning has experienced significant growth in recent years, resulting in increased energy consumption and carbon emission from the use of GPUs for training deep neural networks (DNNs). Answering the call for sustainability, conventional solutions have attempted to move training jobs to locations or time frames with lower carbon intensity. However, moving jobs to other locations may not always be feasible due to large dataset sizes or data regulations. Moreover, postponing training can negatively impact application service quality because the DNNs backing the service are not updated in a timely fashion. In this work, we present a practical solution that reduces the carbon footprint of DNN training without migrating or postponing jobs. Specifically, our solution observes real-time carbon intensity shifts during training and controls the energy consumption of GPUs, thereby reducing carbon footprint while maintaining training performance. Furthermore, in order to proactively adapt to shifting carbon intensity, we propose a lightweight machine learning algorithm that predicts the carbon intensity of the upcoming time frame. Our solution, Chase, reduces the total carbon footprint of training ResNet-50 on ImageNet by 13.6% while only increasing training time by 2.5%.

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