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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.
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 perf
Improving datacenter operations is vital for the digital society. We posit that doing so requires our community to shift, from operational aspects taken in isolation to holistic analysis of datacenter resources, energy, and workloads. In turn, this s
Popular distributed ledger technology (DLT) systems using proof-of-work (PoW) for Sybil attack resistance have extreme energy requirements, drawing stern criticism from academia, businesses, and the media. DLT systems building on alternative consensu
Understanding and tuning the performance of extreme-scale parallel computing systems demands a streaming approach due to the computational cost of applying offline algorithms to vast amounts of performance log data. Analyzing large streaming data is
This paper presents BigDL (a distributed deep learning framework for Apache Spark), which has been used by a variety of users in the industry for building deep learning applications on production big data platforms. It allows deep learning applicatio