Abstract
High-performance computational physics has been instrumental in advancing scientific research by regularly providing breakthroughs in speed, accuracy and modelling fidelity. This Perspective highlights the contributions of physicists to the development of high-performance computing infrastructure, algorithms and applications from the early days of computing to the exascale era. We recall the pioneering work of Fermi and von Neumann, who set directions and laid foundations for computational science and examine the ongoing impact of physicists in overcoming current challenges in high-performance computing, such as energy consumption and data storage. As we celebrate milestones such as exascale computing and generative artificial intelligence, it is inspiring to recognize the enduring influence of physicists in driving technological innovations and ensuring the future progress of computational science.
This is a preview of subscription content, access via your institution
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 12 digital issues and online access to articles
$99.00 per year
only $8.25 per issue
Buy this article
- Purchase on SpringerLink
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
Similar content being viewed by others
References
De Marco, G., Mainetto, G., Pisani, S. & Savino, P. The early computers of Italy. IEEE Ann. Hist. Comput. 21, 28–36 (1999).
Von Neumann, J. First draft of a report on the EDVAC. IEEE Ann. Hist. Comput. 15, 27–75 (1993).
Von Neumann, J. & Goldstine, H. H. Numerical inverting of matrices of high order. Bull. Amer. Math. Soc. 53, 1021–1099 (1947).
Von Neumann, J. & Richtmyer, R. D. A method for the numerical calculation of hydrodynamic shocks. J. Appl. Phys. 21, 232–237 (1950).
Charney, J. G., Fjörtoft, R. & Von Neumann, J. Numerical integration of the barotropic vorticity equation. Tellus 2, 237–254 (1950).
Metropolis, N., Howlett, J. & Rota, G.-C. (eds) A History of Computing in the Twentieth Century (Academic, 1980).
Adler, B. (ed.) Special Purpose Computers (Academic, 1988).
Battimelli, G., Ciccotti, G. & Greco, P. Computer Meets Theoretical Physics (Springer, 2020).
Fox, G., Williams, R. & Messina, P. Parallel Computing Works (Morgan-Kaufmann, 1994).
Vetter, J. Contemporary High Performance Computing (Chapman and Hall/CRC, 2017).
Eijkhout, V. The Art of HPC Volumes 1–4 https://theartofhpc.com/ (The Art of HPC, 2022).
NVIDIA. NVIDIA H100 Tensor Core GPU. NVIDIA https://www.nvidia.com/en-us/data-center/h100/ (2024).
Google. An in-depth look at Google’s first tensor processing unit (TPU). Google https://cloud.google.com/blog/products/ai-machine-learning/an-in-depth-look-at-googles-first-tensor-processing-unit-tpu (2017).
ACM. Artifact review and badging. Association for Computing Machinery https://www.acm.org/publications/policies/artifact-review-badging (2020).
Park, S. Direct numerical simulation for lid-driven cavity under various Reynolds numbers in fully staggered grid. Phys. Fluids 35, 115110 (2023).
Strohmaier, E., Meuer, H. W., Dongarra, J. & Simon, H. D. The TOP500 list and progress in high-performance computing. Computer 48, 42–49 (2015).
Dongarra, J., Heroux, M. A. & Luszczek, P. High-performance conjugate-gradient benchmark: a new metric for ranking high-performance computing systems. Int. J. High Perform. Comput. Appl. 30, 3–10 (2016).
Dongarra, J. & Luszczek, P. HPL-MxP https://hpl-mxp.org/ (2024).
Graph500 https://graph500.org/ (2024).
Green500 https://top500.org/lists/green500/ (2024).
Io500 https://io500.org/ (2024).
NERSC. Al Trivelpiece and the origins of NERSC. National Energy Research Scientific Computing Center https://www.nersc.gov/news-publications/nersc-news/nersc-center-news/nersc-40th-anniversary/al-trivelpiece-and-the-origins-of-nersc/ (2014).
Stone, J. M. & Norman, M. L. ZEUS-2D: a radiation magnetohydrodynamics code for astrophysical flows in two space dimensions. I — the hydrodynamic algorithms and tests. Astrophys. J. Suppl. Ser. 80, 753–790 (1992).
Barnes, J. E. & Hut, P. Error analysis of a tree code. Astrophys. J. Suppl. Ser. 70, 389–417 (1989).
Goodale, T. et al. The Cactus framework and toolkit: design and applications. In Vector and Parallel Processing — VECPAR’2002, 5th International Conference, Lecture Notes in Computer Science 197–227 (Springer, 2003).
Dubey, A. et al. Evolution of FLASH, a multi-physics scientific simulation code for high-performance computing. Int. J. High Perform. Comput. Appl. 28, 225–237 (2013).
Berners-Lee, T. Information management: a proposal. w3 archive https://www.w3.org/History/1989/proposal.html (2024).
CompaniesMarketCap. Largest companies by market cap. CompaniesMarketCap https://companiesmarketcap.com/ (2024).
Boyle, P. et al. QCDOC: a 10 teraflops computer for tightly-coupled calculations. In SC ’04: Proceedings of the 2004 ACM/IEEE Conference on Supercomputing 40 (2004).
NSTMF. National medal of technology and innovation. National Science and Technology Medals Foundation https://nationalmedals.org/laureate/ibm-corporation-2/ (2024).
Bell, G., Bailey, D. H., Dongarra, J., Karp, A. H. & Walsh, K. A look back on 30 years of the Gordon Bell Prize. Int. J. High Perform. Comput. Appl. 31, 469–484 (2017).
Warren, M. & Salmon, J. A parallel hashed Oct-Tree N-body algorithm. In Supercomputing ‘93: Proceedings of the 1993 ACM/IEEE Conference on Supercomputing 12–21 (ACM, 1993).
Schweber, S. & Wächter, M. Complex systems, modelling and simulation. Stud. Hist. Philos. Sci. B 31, 583–609 (2000).
Keyes, D., Colella, P., Dunning Jr, T. & Gropp, W. A science-based case for large-scale simulation (US DOE, 2003).
Dennard, R. et al. Design of ion-implanted MOSFET’s with very small physical dimensions. IEEE J. Solid State Circuits 9, 256–268 (1974).
Moore, G. E. Cramming more components onto integrated circuits, reprinted from Electronics, volume 38, number 8, April 19, 1965, pp.114 ff. IEEE Solid State Circuits Soc. Newslett. 11, 33–35 (2006).
Feynman, R. P. Simulating physics with computers. Int. J. Theor. Phys. 21, 467–488 (1982).
Chirigati, F. The universe’s expansion in the eyes of computers. Nat. Comput. Sci. 2, 545–547 (2022).
Acknowledgements
The authors thank Iulia Georgescu for historical observations and her motivation for this article.
Author information
Authors and Affiliations
Contributions
The authors contributed equally to all aspects of the article.
Corresponding authors
Ethics declarations
Competing interests
The authors declare no competing interests.
Peer review
Peer review information
Nature Reviews Physics thanks Junichiro Makino and the other, anonymous, referee(s) for their contribution to the peer review of this work.
Additional information
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Dongarra, J., Keyes, D. The co-evolution of computational physics and high-performance computing. Nat Rev Phys 6, 621–627 (2024). https://doi.org/10.1038/s42254-024-00750-z
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/s42254-024-00750-z