Modeling the Carbon Footprint of HPC: The Top 500 and EasyC
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arXiv
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| Format: | Preprint |
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2025
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| author | Rao, Varsha Chien, Andrew A. |
| author_facet | Rao, Varsha Chien, Andrew A. |
| contents | Climate change is a critical concern for HPC systems, but GHG protocol carbon-emission accounting methodologies are difficult for a single system, and effectively infeasible for a collection of systems. As a result, there is no HPC-wide carbon reporting, and even the largest HPC sites do not do GHG protocol reporting.
We assess the carbon footprint of HPC, focusing on the Top 500 systems. The key challenge lies in modeling the carbon footprint with limited data availability.
With the disclosed top500 website data, and using a new tool, EasyC, we were able to model the operational carbon of 391 HPC systems and the embodied carbon of 283 HPC systems. We further show how this coverage can be enhanced by exploiting additional public information. With improved coverage, then interpolation is used to produce the first carbon footprint estimates of the Top 500 HPC systems. They are 1.4 million MT CO2e operational carbon (1 Year) and 1.9 million MT CO2e embodied carbon. We also project how the Top 500's carbon footprint will increase through 2030.
A key enabler is the EasyC tool which models carbon footprint with only a few data metrics. We explore availability of data and enhancement, showing that coverage can be increased to 98% of Top 500 systems for operational and 80.8% of the systems for embodied emissions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_13583 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Modeling the Carbon Footprint of HPC: The Top 500 and EasyC Rao, Varsha Chien, Andrew A. Distributed, Parallel, and Cluster Computing Climate change is a critical concern for HPC systems, but GHG protocol carbon-emission accounting methodologies are difficult for a single system, and effectively infeasible for a collection of systems. As a result, there is no HPC-wide carbon reporting, and even the largest HPC sites do not do GHG protocol reporting. We assess the carbon footprint of HPC, focusing on the Top 500 systems. The key challenge lies in modeling the carbon footprint with limited data availability. With the disclosed top500 website data, and using a new tool, EasyC, we were able to model the operational carbon of 391 HPC systems and the embodied carbon of 283 HPC systems. We further show how this coverage can be enhanced by exploiting additional public information. With improved coverage, then interpolation is used to produce the first carbon footprint estimates of the Top 500 HPC systems. They are 1.4 million MT CO2e operational carbon (1 Year) and 1.9 million MT CO2e embodied carbon. We also project how the Top 500's carbon footprint will increase through 2030. A key enabler is the EasyC tool which models carbon footprint with only a few data metrics. We explore availability of data and enhancement, showing that coverage can be increased to 98% of Top 500 systems for operational and 80.8% of the systems for embodied emissions. |
| title | Modeling the Carbon Footprint of HPC: The Top 500 and EasyC |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2509.13583 |