Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Comment
  • Published:

The ecological impact of high-performance computing in astrophysics

Nature Astronomy volume 4, pages 819–822 (2020)Cite this article

Subjects

Computer use in astronomy continues to increase, and so also its impact on the environment. To minimize the effects, astronomers should avoid interpreted scripting languages such as Python, and favour the optimal use of energy-efficient workstations.

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Carbon production of a number of common activities among astronomers.
Fig. 2: Energy to solution as a function of code performance.
Fig. 3: Programming language efficiency as a function of the time to solution.

References

  1. Ossendrijver, M. Science 351, 482–484 (2016).

    Article  ADS  MathSciNet  Google Scholar 

  2. Stevens, A. R. H., Bellstedt, S., Elahi, P. J. & Murphy, M. T. Nat. Astron. https://doi.org/10.1038/s41550-020-1169-1 (2020).

  3. Achten, W. M., Almeida, J. & Muys, B. Ecol. Indic. 34, 352–355 (2013).

    Article  Google Scholar 

  4. Portegies Zwart, S. & McMillan, S. Astrophysical Recipes: The Art of AMUSE (IOP Publishing, 2018).

  5. Paxton, B. et al. Astrophys. J. Suppl. Ser. 192, 3–38 (2011).

    Article  ADS  Google Scholar 

  6. Portegies Zwart, S. F. & Verbunt, F. Astron. Astrophys. 309, 179–196 (1996).

    ADS  Google Scholar 

  7. Gaburov, E., Bédorf, J. & Portegies Zwart, S. Procedia Comput. Sci. 1, 1119–1127 (2010).

    Article  Google Scholar 

  8. Barnes, J. & Hut, P. Nature 324, 446–449 (1986).

    Article  ADS  Google Scholar 

  9. Hofmann, J., Hager, G. & Fey, D. P. In High Performance Computing (eds Yokota, R. et al.) 22–43 (Springer, 2018).

  10. Feng, W. & Cameron, K. Computer 40, 50–55 (2007).

    Article  Google Scholar 

  11. Van Rossum, G. & Drake, F. L. Jr Python Reference Manual (Centrum voor Wiskunde en Informatica, 1995).

  12. Pereira, R. et al. In Proc. 10th ACM SIGPLAN Int. Conf. on Software Language Engineering SLE 2017 256–267 (Association for Computing Machinery, 2017).

  13. Lam, S. K., Pitrou, A. & Seibert, S. In Proc. Second Workshop on the LLVM Compiler Infrastructure in HPC LLVM ’15 1–6 (Association for Computing Machinery, 2015).

  14. Oliphant, T. E. A Guide to NumPy Vol. 1 (Trelgol Publishing, 2006).

  15. Portegies Zwart, S. F., Belleman, R. G. & Geldof, P. M. New Astron. 12, 641–650 (2007).

    Article  ADS  Google Scholar 

  16. Bédorf, J. et al. In Proc. Int. Conf. for High Performance Computing, Networking, Storage and Analysis SC ’14 54–65 (IEEE, 2014).

  17. Advanced LIGO Reference Design: LIGO M060056-v2 (LIGO Scientific Collaboration, 2011).

  18. D’Addario, L. in Proceedings of Exascale Radio Astronomy 302.01 (American Astronomical Society, 2014).

  19. Heggie, D. C. & Mathieu, R. D. in The Use of Supercomputers in Stellar Dynamics (eds Hut, P. & McMillan, S. L. W.) 233–235 (Springer, 1986).

  20. Wittmann, M., Hager, G., Zeiser, T., Treibig, J. & Wellein, G. Concurr. Comput. Pract. Exper. 28, 2295–2315 (2016).

    Article  Google Scholar 

  21. Heinrich, F. C. et al. in IEEE International Conference on Cluster Computing 92–102 (IEEE, 2017).

  22. Hunter, J. D. Comput. Sci. Eng. 9, 90–95 (2007).

    Article  Google Scholar 

  23. Cutress, I. The Intel Xeon W-3175X review: 28 unlocked cores, $2999. AnandTech https://www.anandtech.com/show/13748 (30 January 2019).

  24. Portegies Zwart, S. & Boekholt, T. Astrophys. J. Lett. 785, L3–L7 (2014).

    Article  ADS  Google Scholar 

Download references

Acknowledgements

I thank A. Allen for providing data on the ASCL language usage and L. Butscher for comments and inviting me to present this discussion at the 2020 European Astronomical Society conference. Part of this work was performed using resources provided by the Academic Leiden Interdisciplinary Cluster Environment (Alice), TITAN (LANL) and LGM-II (NWO grant number 621.016.701) We used the Python, Matplotlib, NumPy, Numba and AMUSE open-source packages.

Author information

Authors and Affiliations

  1. Leiden Observatory, Leiden University, Leiden, the Netherlands

    Simon Portegies Zwart

Authors
  1. Simon Portegies Zwart
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Simon Portegies Zwart.

Ethics declarations

Competing interests

The author declares no competing interests.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Portegies Zwart, S. The ecological impact of high-performance computing in astrophysics. Nat Astron 4, 819–822 (2020). https://doi.org/10.1038/s41550-020-1208-y

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41550-020-1208-y

This article is cited by

Search

Advanced search

Quick links

Nature Briefing AI and Robotics

Sign up for the Nature Briefing: AI and Robotics newsletter — what matters in AI and robotics research, free to your inbox weekly.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing: AI and Robotics