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Understanding the Energy Demands of Advanced Computing
Data centers and advanced computational systems represent significant energy consumers. A recent analysis conducted by Lawrence Berkeley National Laboratory indicated that U.S. data centers consumed approximately 4.4% of the nation’s total electricity in 2023, with projections suggesting this figure could potentially triple by 2028. One major contributor to this surge is the extensive adoption of artificial intelligence (AI), which is already affecting consumer utility costs.
The Quest for Energy Efficiency in Computing
Researchers at the National Renewable Energy Laboratory (NREL) are actively exploring ways to mitigate the energy expenses associated with computing. In addition to developing strategies for enhancing energy efficiency, they aim to share their findings and tools with industry software engineers.
Among their contributions is “A Beginner’s Guide to Power and Energy Measurement and Estimation,” a newly released report co-developed with Intel, which highlights essential factors for machine learning professionals interested in utilizing energy measurement tools and interpreting consumption estimates effectively.
“As AI continues to influence various sectors, its escalating impact on power consumption represents a common issue that requires our collective action,” stated Hilary Egan, data scientist at NREL and principal author of the guide. “We crafted this resource to introduce AI developers to energy estimation concepts that facilitate more sustainable decisions within computing.”
Tackling AI’s Environmental Footprint through Collaboration
NREL’s dedication toward diminishing both computing-related and AI-driven energy use led them in 2022 to establish the Joint Institute for Strategic Energy Analysis (JISEA) Green Computing Catalyzer. This initiative is part of JISEA’s broader Catalyzer Program aimed at identifying promising investment opportunities while uniting researchers, academic institutions, and corporate partners focused on strategies that minimize computing’s environmental impact.
Over time, the Green Computing Catalyzer has played a pivotal role in measuring and documenting machine learning’s energy requirements as well as NREL’s advanced computing systems—all aimed at fostering increased transparency within the field of computation. The significance of these findings has attracted interest from Intel, who oversees its own suite of Responsible AI programs.
This collaboration between Intel and NREL culminated in “A Beginner’s Guide to Power and Energy Measurement and Estimation,” designed specifically for developers looking to better manage their system’s sustainability through effective energy assessment practices.
Pursuing Sustainable Solutions: A Call from Industry Leaders
“At Intel, sustainability has always been central—both prior to advancements in AI technology as well as post-implementation,” remarked Ronak Singhal, senior fellow within Intel’s Datacenter division focused on AI initiatives. “The input from NREL was instrumental in producing our jointly created guide which empowers developers by providing them with crucial skills needed for responsible measurements—a critical step forward towards achieving sustainability goals concerning artificial intelligence.”
A Comprehensive Framework for Assessing Energy Use
An increasing number of developers involved with machine learning frameworks are beginning to include considerations regarding energy usage into their model designs; however, there remains an absence of standard protocols encompassing all levels of computational tasks. The new guide seeks not only accessibility but also aims at standardizing measurement techniques across various domains—including system architecture down through individual coding strands.
This report serves as a structured approach outlining methods used domestically or commercially while elaborating on challenges encountered when deciphering measurement results into actionable insights.
Real-life scenarios detailing implementations help illustrate how differing practices can optimize productivity across technological platforms.
Navigating Through Workflow Assessments
The guide assists practitioners by initiating their inquiries around key objectives before guiding them smoothly into selecting appropriate tools necessary for gathering relevant data—which must subsequently undergo analysis followed by interpretative scrutiny related back towards initial queries prompted.
Ultimately leading users toward assessing adequacy regarding analyses thereby identifying possible refinements or adjustments required during subsequent iterations carried forth hereafterAn Integrated Approach Towards Innovations Driven By Research Findings
“The Catalyzer Program addresses significant integrated challenges affiliated directly tied against sustainability engagement efforts,” said Kristin Wegner Guilfoyle who leads program initiatives under NREL directions.
“This roadmap aligns research-derived insights about resource-efficient computations alongside innovation generated through diverse developer perspectives.”
