Mitigating the Environmental Impact of High-Performance Computing Data Centers
DOI:
https://doi.org/10.54097/y0yb9r53Keywords:
High-Powered Computing, Data Center, Electricity Consumption, Generalized Linear Model, Polynomial RegressionAbstract
While High Performance Computing (HPC) is critical for scientific breakthroughs and AI development, its growing energy use (often dependent on fossil fuels) requires a detailed environmental impact analysis. This study addresses the ever-increasing ecological challenges posed by high-performance computing (HPC) data centers by developing a model to quantify and predict their energy consumption and carbon emissions. Our model estimates the annual energy consumption of HPC data centers globally using data on the size and capacity of small and massive data centers. It distinguishes between theoretical maximum and actual energy consumption based on average utilization. The results show an upward trend in carbon emissions associated with HPC, which are projected to reach 5.81 x 1017 grams of carbon dioxide by 2030. As a result, we propose a series of recommendations to reduce the environmental impact of HPC at both the technical and policy levels in favor of promoting a healthy and sustainable environment.
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[1] 2024 Global Data Center Market Comparison. (n.d.). Page 30. https://cushwake.cld.bz/2024-Global-Data-Center-Market-Comparison/30/.
[2] AI is poised to drive a 160% increase in data center power demand. (2024, May 14). Goldman Sachs. https://www.goldmansachs.com/insights/articles/AI-poised-to-drive-160-increase-in-power-demand.
[3] Author, N. (2023, June 2). The huge carbon footprint of large-scale computing – Physics World. Physics World. https://physicsworld.com/a/the-huge-carbon-footprint-of-large-scale-computing/.
[4] Bautista, D. (2022, March 24). Water usage for data center cooling and electricity. AKCP Remote Sensor Monitoring | Data Center Monitoring. https://www.akcp.com/blog/water-usage-for-data-center-cooling-and-electricity-generation/.
[5] National Knowledge Infrastructure. (n.d.). https://www.cnki.net/.
[6] Energy.gov. (n.d.). The Department of Energy’s Energy.gov. https://www.energy.gov/.
[7] Global e-Waste Monitor 2024: Electronic Waste Rising Five Times Faster than Documented E-waste Recycling. (n.d.). UNITAR. https://unitar.org/about/news-stories/press/global-e-waste-monitor-2024-electronic-waste-rising-five-times-faster-documented-e-waste-recycling.
[8] High-Performance Computing data center power usage effectiveness. (n.d.). Computational Science | NREL. https://www.nrel.gov/computational-science/measuring-efficiency-pue.html.
[9] Santos, D. C. D. (2022, November 14). Understanding the energy consumption of HPC scale Artificial intelligence. arXiv.org. https://arxiv.org/abs/2212.00582.
[10] Wengel, G. (n.d.). A Guide to Carbon-Aware Computing | Insights & Sustainability | ClimatIQ. https:// www. climatiq.io/blog/a-guide-to-carbon-aware-computing.
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