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Register a new userA Framework for Auditing Data Center Energy Usage and Mitigating Environmental Footprint
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Added by
Justin Gould
Publication date
2021
fields
Informatics Engineering
and research's language is
English
Authors
Justin Gould
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As the Data Science field continues to mature, and we collect more data, the demand to store and analyze them will continue to increase. This increase in data availability and demand for analytics will put a strain on data centers and compute clusters-with implications for both energy costs and emissions. As the world battles a climate crisis, it is prudent for organizations with data centers to have a framework for combating increasing energy costs and emissions to meet demand for analytics work. In this paper, I present a generalized framework for organizations to audit data centers energy efficiency to understand the resources required to operate a given data center and effective steps organizations can take to improve data center efficiency and lower the environmental impact.
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Training Deep Neural Networks (DNNs) is resource-intensive and time-consuming. While prior research has explored many different ways of reducing DNN training time, the impact of input data pipeline, i.e., fetching raw data items from storage and performing data pre-processing in memory, has been relatively unexplored. This paper makes the following contributions: (1) We present the first comprehensive analysis of how the input data pipeline affects the training time of widely-used computer vision and audio Deep Neural Networks (DNNs), that typically involve complex data preprocessing. We analyze nine different models across three tasks and four datasets while varying factors such as the amount of memory, number of CPU threads, storage device, GPU generation etc on servers that are a part of a large production cluster at Microsoft. We find that in many cases, DNN training time is dominated by data stall time: time spent waiting for data to be fetched and preprocessed. (2) We build a tool, DS-Analyzer to precisely measure data stalls using a differential technique, and perform predictive what-if analysis on data stalls. (3) Finally, based on the insights from our analysis, we design and implement three simple but effective techniques in a data-loading library, CoorDL, to mitigate data stalls. Our experiments on a range of DNN tasks, models, datasets, and hardware configs show that when PyTorch uses CoorDL instead of the state-of-the-art DALI data loading library, DNN training time is reduced significantly (by as much as 5x on a single server).
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