Understanding Power and Energy Utilization in Large Scale Production Physics Simulation Codes

Ryujin, Brian S.; Vargas, Anielle de; Karlin, Ian; Dawson, Shawn; Weiss, Kenneth; Bertsch, Adam; McKinley, Michael Scott; Collette, Michael R.; Hammond, Simon David; Pedretti, Kevin; Rieben, Robert N.

doi:10.2172/1838264

2022

DOI: 10.2172/1838264

|View full text |Cite

Understanding Power and Energy Utilization in Large Scale Production Physics Simulation Codes

Brian S. Ryujin

Anielle de Vargas

Ian Karlin

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Estimating the carbon footprint of computational fluid dynamics

Horwitz

2024

Physics of Fluids

View full text Add to dashboard Cite

Computational resources have grown exponentially in the past few decades. These machines make possible research and design in fields as diverse as medicine, astronomy, and engineering. Despite ever-increasing computational capabilities, direct simulation of complex systems has remained challenging owing to the degrees of freedom involved. At the cusp of exascale computing, high-resolution simulation of practical problems with minimal model assumptions may soon experience a renaissance. However, growing reliance on modern computers comes at the cost of a growing carbon footprint. To illustrate this, we examine historic computations in fluid dynamics where larger computers have afforded the opportunity to simulate flows at increasingly relevant Reynolds numbers. Under a variety of flow configurations, the carbon footprint of such simulations is found to scale roughly with the fourth power of Reynolds number. This is primarily explained by the computation cost in core-hours, which is also described by similar scaling, though regional differences in renewable energy use also play a role. Using the established correlation, we examine a large database of simulations to develop estimates for the carbon footprint of computational fluid dynamics in a given year. Collectively, the analysis provides an additional benchmark for new computations where, in addition to balancing considerations of model fidelity, carbon footprint should also be considered.

show abstract

Estimating the carbon footprint of computational fluid dynamics

Horwitz

2024

Physics of Fluids

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Understanding Power and Energy Utilization in Large Scale Production Physics Simulation Codes

Cited by 1 publication

References 0 publications

Estimating the carbon footprint of computational fluid dynamics

Estimating the carbon footprint of computational fluid dynamics

Contact Info

Product

Resources

About