Abstract
The challenging computational requirements of nuclear fusion research arise from the multiple timescales and space scales involved in the physics and engineering processes of a fusion device. Owing to the intrinsic and complex interconnections of these processes, the complex multiphysics and multiscale nature of fusion simulations require the capabilities of cutting-edge supercomputers. Advances in supercomputing enable a move towards larger-scale, higher-fidelity full fusion reactor digital models that capture not only the plasma core and edge physics but also interactions with materials and engineering aspects, such as fusion reactor walls and cooling systems. This Perspective discusses the main opportunities that fusion codes face in the transition to the emerging exascale systems and beyond, and the challenges that remain to be overcome.
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References
IAEA World Fusion Outlook 2024 (International Atomic Energy Agency, 2024).
Meschini, S. et al. Review of commercial nuclear fusion projects. Front. Energy Res. https://doi.org/10.3389/fenrg.2023.1157394 (2023).
World Survey of Fusion Devices 2022 (International Atomic Energy Agency, 2023).
Knaster, J., Moeslang, A. & Muroga, T. Materials research for fusion. Nat. Phys. 12, 424–434 (2016).
Nordlund, K. et al. Primary radiation damage: a review of current understanding and models. J. Nucl. Mater. 512, 450–479 (2018).
Federici, G. et al. An overview of the EU breeding blanket design strategy as an integral part of the DEMO design effort. Fusion Eng. Des. 141, 30–42 (2019).
Boullon, R., Jaboulay, J.-C. & Aubert, J. Molten salt breeding blanket: investigations and proposals of pre-conceptual design options for testing in DEMO. Fusion Eng. Des. 171, 112707 (2021).
Wolf, M. J. et al. High temperature superconductors for fusion applications and new developments for the HTS CroCo conductor design. Fusion Eng. Des. 172, 112739 (2021).
Maggi, C. et al. Overview of T and D–T results in JET with ITER-like wall. Nucl. Fusion 64, 112012 (2024).
Zylstra, A. et al. Burning plasma achieved in inertial fusion. Nature https://doi.org/10.1038/s41586-021-04281-w (2022).
The Global Fusion Industry in 2024 (Fusion Industry Association, 2024).
Litaudon, X. et al. EUROfusion-theory and advanced simulation coordination (E-TASC): programme and the role of high performance computing. Plasma Phys. Control. Fusion 64, 034005 (2022).
Garcia, J. et al. Modelling performed for predictions of fusion power in JET DTE2: overview and lessons learnt. Nucl. Fusion 63, 112003 (2023).
Marinak, M. M. et al. How numerical simulations helped to achieve breakeven on the NIF. Phys. Plasmas 31, 070501 (2024).
Loarte, A. Modelling needs to support the ITER Research Plan and role of HPC. 1st Spanish Fusion HPC Workshop https://hpcfusion.bsc.es/2020/media/ (2020).
Imbeaux, F. et al. Design and first applications of the ITER integrated modelling and analysis suite. Nucl. Fusion 55, 123006 (2015).
Pinches, S. D. et al. Integrated modelling & analysis suite: developments to address ITER needs. In Proc. 28th IAEA Fusion Energy Conference https://conferences.iaea.org/event/214/contributions/17232/ (IAEA, 2021).
A survey of artificial intelligence and high performance computing applications to fusion commercialization. Clean Air Task Force https://www.catf.us/resource/a-survey-of-artificial-intelligence-and-high-performance-computing-applications-to-fusion-commercialization/ (2024).
Litaudon, X. et al. Long plasma duration operation analyses with an international multi-machine (tokamaks and stellarators) database. Nucl. Fusion 64, 015001 (2023).
Jenko, F. Accelerating fusion energy research through HPC. 2nd Fusion HPC Workshop 2021 https://hpcfusion.bsc.es/2021/wp-content/uploads/sites/3/2021/12/Frank-Jenko_presentation.pdf (2021).
Wehinger, G. D. et al. Quo vadis multiscale modeling in reaction engineering? — A perspective. Chem. Eng. Res. Des. 184, 39–58 (2022).
Thornton, J. E. The CDC 6600 Project. Ann. Hist. Comput. 2, 338–348 (1980).
Moore, G. E. Cramming more components onto integrated circuits. Electronics 38, 114–117 (1965).
Atchley, S. et al. Frontier: exploring exascale. In Proc. International Conference for High Performance Computing, Networking, Storage and Analysis https://doi.org/10.1145/3581784.3607089 (ACM, 2023).
Grieves, M. & Vickers, J. in Transdisciplinary Perspectives on Complex Systems: New Findings and Approaches (eds Kahlen, F.-J. et al.) 85–113 (Springer, 2016).
Das, R. S. & Gupta, V. A systematic literature review on graphics processing unit accelerated realm of high-performance computing. Int. J. Comput. Eng. 5, 10–21 (2024).
TOP500 list - November 2024. TOP500 https://www.top500.org/lists/top500/list/2024/11/ (2024).
MPI: A Message-Passing Interface Standard Version 4.0. www.mpi-forum.org/docs/mpi-4.0/mpi40-report.pdf (Message Passing Interface Forum, 2021).
Flynn, M. Very high-speed computing systems. Proc. IEEE 54, 1901–1909 (1966).
Apollonatos, A. Helios supercomputer ready to bite the bytes. ITER Newsline https://www.iter.org/node/20687/helios-supercomputer-ready-bite-bytes (2012).
Iannone, F. et al. MARCONI-FUSION: the new high performance computing facility for European nuclear fusion modelling. Fusion Eng. Des. 129, 354–358 (2018).
CUDA zone - library of resources. NVIDIA https://developer.nvidia.com/cuda-zone (2023).
Herdman, J. et al. Achieving portability and performance through OpenACC. In Proc. 2014 First Workshop on Accelerator Programming using Directives 19–26 (IEEE, 2014).
Messina, P. The exascale computing project. Comput. Sci. Eng. 19, 63–67 (2017).
Sáez, X. et al. The Advanced Computing Hub at BSC: improving fusion codes following modern software engineering standards. Plasma Phys. Control. Fusion 66, 075014 (2024).
Suchyta, E. et al. The Exascale Framework for High Fidelity coupled Simulations (EFFIS): enabling whole device modeling in fusion science. Int. J. High Perform. Comput. Appl. 36, 106–128 (2022).
George, T. & Sarin, V. in Encyclopedia of Parallel Computing (ed. Padua, D.) 578–587 (Springer, 2011).
Kleiber, R. et al. EUTERPE: a global gyrokinetic code for stellarator geometry. Comput. Phys. Commun. 295, 109013 (2024).
Giacomin, M. et al. The GBS code for the self-consistent simulation of plasma turbulence and kinetic neutral dynamics in the tokamak boundary. J. Comput. Phys. 463, 111294 (2022).
Garcia-Gasulla, M., Vinyals-Ylla-Catala, J., Romazanov, J., Baumann, C. & Matveev, D. Performance analysis and optimizations of ERO2.0 fusion code. In Proc. Platform for Advanced Scientific Computing Conference https://doi.org/10.1145/3659914.3659932 (ACM, 2024).
Tskhakaya, D. & Schneider, R. Optimization of PIC codes by improved memory management. J. Comput. Phys. 225, 829–839 (2007).
Plimpton, S. J. et al. Direct simulation Monte Carlo on petaflop supercomputers and beyond. Phys. Fluids 31, 086101 (2019).
Novak, A., Brooks, H., Shriwise, P. & Davis, A. Monte Carlo multiphysics simulation on adaptive unstructured mesh geometry. Nucl. Eng. Des. 429, 113589 (2024).
Gutierrez-Milla, A. et al. New high performance computing software for multiphysics simulations of fusion reactors. Fusion Eng. Des. 136, 639–644 (2018).
Vázquez, M. et al. Alya: multiphysics engineering simulation toward exascale. J. Comput. Sci. 14, 15–27 (2016).
Soba, A. et al. Validations of the radiation transport module NEUTRO: a deterministic solver for the neutron transport equation. Fusion Eng. Des. 169, 112497 (2021).
Goldberg, E., i Duxans, M. C., Gelabert, O. O., Mantsinen, M. J. & Soba, A. Validating NEUTRO, a deterministic finite element neutron transport solver for fusion applications, with literature tests, experimental benchmarks and other neutronic codes. Plasma Phys. Control. Fusion 64, 104006 (2022).
Soba, A. et al. A high-performance electromagnetic code to simulate high-temperature superconductors. Fusion Eng. Des. 201, 114282 (2024).
Brooks, H. & Davis, A. Scalable multi-physics for fusion reactors with AURORA. Plasma Phys. Control. Fusion 65, 024002 (2022).
Badalassi, V. et al. FERMI: fusion energy reactor models integrator. Fusion Sci. Technol. 79, 345–379 (2023).
Permann, C. J. et al. MOOSE: enabling massively parallel multiphysics simulation. SoftwareX 11, 100430 (2020).
Alexander, F. et al. Exascale applications: skin in the game. Phil. Trans. R. Soc. A 378, 20190056 (2020).
Tang, W. M. Scientific and computational challenges of the fusion simulation project (FSP). J. Phys. Conf. Ser. 125, 012047 (2008).
Davis, A. High performance multiphysics driven design for fusion systems. 4th Spanish Fusion HPC Workshop https://hpcfusion.bsc.es/2023/media/ (2023).
Emoto, M., Ohdachi, S., Watanabe, K., Sudo, S. & Nagayama, Y. Server for experimental data from LHD. Fusion Eng. Des. 81, 2019–2023 (2006).
Kovač, M. et al. in HPC, Big Data, and AI Convergence Towards Exascale (eds Terzo, O. & Martinovič, J.) 273–290 (CRC Press, 2022).
Mantovani, F. et al. Software development vehicles to enable extended and early co-design: a RISC-V and HPC case of study. In Proc. International Conference on High Performance Computing 526–537 (Springer, 2023).
Blancafort, M., Ferrer, R., Houzeaux, G., Garcia-Gasulla, M. & Mantovani, F. Exploiting long vectors with a CFD code: a co-design show case. In Proc. 2024 IEEE International Parallel and Distributed Processing Symposium (IPDPS) 453–464 (IEEE, 2024).
Zidar, J., Matic, T., Aleksi, I. & Hocenski, Z. Dynamic voltage and frequency scaling as a method for reducing energy consumption in ultra-low-power embedded systems. Electronics 13, 826 (2024).
Belli, E. & Candy, J. Implications of advanced collision operators for gyrokinetic simulation. Plasma Phys. Control. Fusion 59, 045005 (2017).
Frerichs, H. et al. Detachment in fusion plasmas with symmetry breaking magnetic perturbation fields. Phys. Rev. Lett. 125, 155001 (2020).
Kornilov, V., Kleiber, R., Hatzky, R., Villard, L. & Jost, G. Gyrokinetic global three-dimensional simulations of linear ion-temperature-gradient modes in Wendelstein 7-X. Phys. Plasmas 11, 3196–3202 (2004).
Ricci, P. et al. Simulation of plasma turbulence in scrape-off layer conditions: the GBS code, simulation results and code validation. Plasma Phys. Control. Fusion 54, 124047 (2012).
Jenko, F., Dorland, W., Kotschenreuther, M. & Rogers, B. Electron temperature gradient driven turbulence. Phys. Plasmas 7, 1904–1910 (2000).
Maurer, M. et al. GENE-3D: a global gyrokinetic turbulence code for stellarators. J. Comput. Phys. 420, 109694 (2020).
Michels, D., Stegmeir, A., Ulbl, P., Jarema, D. & Jenko, F. GENE-X: a full-f gyrokinetic turbulence code based on the flux-coordinate independent approach. Comput. Phys. Commun. 264, 107986 (2021).
Mandell, N., Hakim, A., Hammett, G. & Francisquez, M. Electromagnetic full-gyrokinetics in the tokamak edge with discontinuous Galerkin methods. J. Plasma Phys. 86, 905860109 (2020).
Watanabe, T.-H. & Sugama, H. Velocity–space structures of distribution function in toroidal ion temperature gradient turbulence. Nucl. Fusion 46, 24 (2005).
Stegmeir, A. et al. GRILLIX: a 3D turbulence code based on the flux-coordinate independent approach. Plasma Phys. Control. Fusion 60, 035005 (2018).
Idomura, Y., Urano, H., Aiba, N. & Tokuda, S. Study of ion turbulent transport and profile formations using global gyrokinetic full-f Vlasov simulation. Nucl. Fusion 49, 065029 (2009).
Grandgirard, V. et al. A 5D gyrokinetic full-f global semi-Lagrangian code for flux-driven ion turbulence simulations. Comput. Phys. Commun. 207, 35–68 (2016).
Mandell, N. R. et al. GX: a GPU-native gyrokinetic turbulence code for tokamak and stellarator design. J. Plasma Phys. 90, 905900402 (2024).
Lanti, E. et al. ORB5: a global electromagnetic gyrokinetic code using the PIC approach in toroidal geometry. Comput. Phys. Commun. 251, 107072 (2020).
Sentoku, Y. & Kemp, A. Numerical methods for particle simulations at extreme densities and temperatures: weighted particles, relativistic collisions and reduced currents. J. Comput. Phys. 227, 6846–6861 (2008).
Barnes, M., Parra, F. I. & Landreman, M. stella: an operator-split, implicit–explicit δf-gyrokinetic code for general magnetic field configurations. J. Comput. Phys. 391, 365–380 (2019).
Tskhakaya, D., Matyash, K., Schneider, R. & Taccogna, F. The particle-in-cell method. Contrib. Plasma Phys. 47, 563–594 (2007).
Taccogna, F. et al. Plasma-neutral interaction in kinetic models for the divertor region. Contrib. Plasma Phys. 48, 147–152 (2008).
Romazanov, J. et al. First Monte-Carlo modelling of global beryllium migration in ITER using ERO2.0. Contrib. Plasma Phys. 60, e201900149 (2020).
Wiesenberger, M., Gerrú, R. & Held, M. Numerical evaluation of line, surface and toroidal integrals on level sets of toroidally symmetric functions. J. Comput. Phys. 491, 112407 (2023).
Wiesen, S. et al. The new SOLPS-ITER code package. J. Nucl. Mater. 463, 480–484 (2015).
Bufferand, H. et al. Three-dimensional modelling of edge multi-component plasma taking into account realistic wall geometry. Nucl. Mater. Energy 18, 82–86 (2019).
Komm, M. et al. Particle-in-cell simulations of the plasma interaction with poloidal gaps in the ITER divertor outer vertical target. Nucl. Fusion 57, 126047 (2017).
Ku, S., Chang, C.-S. & Diamond, P. H. Full-f gyrokinetic particle simulation of centrally heated global ITG turbulence from magnetic axis to edge pedestal top in a realistic tokamak geometry. Nucl. Fusion 49, 115021 (2009).
Dudt, D. & Kolemen, E. DESC: a stellarator equilibrium solver. Phys. Plasmas 27, 102513 (2020).
Hindenlang, F., Plunk, G. G. & Maj, O. Computing MHD equilibria of stellarators with a flexible coordinate frame. Plasma Phys. Control. Fusion 67, 045002 (2025).
Lazerson, S. et al. STELLOPT. GitHub https://github.com/princetonuniversity/STELLOPT (2021).
Hudson, S. et al. Computation of multi-region relaxed magnetohydrodynamic equilibria. Phys. Plasmas 19, 112502 (2012).
Hirvijoki, E. et al. ASCOT: solving the kinetic equation of minority particle species in tokamak plasmas. Comput. Phys. Commun. 185, 1310–1321 (2014).
Stotler, D. & Karney, C. Neutral gas transport modeling with DEGAS2. Contrib. Plasma Phys. 34, 392–397 (1994).
Ward, S. et al. Verification and validation of the high-performance Lorentz-orbit code for use in stellarators and tokamaks (LOCUST). Nucl. Fusion 61, 086029 (2021).
Dudson, B., Umansky, M., Xu, X., Snyder, P. & Wilson, H. BOUT++: a framework for parallel plasma fluid simulations. Comput. Phys. Commun. 180, 1467–1480 (2009).
Vlad, G., Briguglio, S., Fogaccia, G. & Zonca, F. Hybrid MHD-gyrokinetic codes: extended models, new implementations and forthcoming applications. In Proc. 12th IAEA Technical Meeting on Energetic Particles in Magnetic Confinement Systems 7–10 (IAEA, 2011).
Huysmans, G. & Czarny, O. MHD stability in X-point geometry: simulation of ELMs. Nucl. Fusion 47, 659 (2007).
Todo, Y. & Sato, T. Linear and nonlinear particle-magnetohydrodynamic simulations of the toroidal Alfvén eigenmode. Phys. Plasmas 5, 1321–1327 (1998).
Lütjens, H. & Luciani, J.-F. The XTOR code for nonlinear 3D simulations of MHD instabilities in tokamak plasmas. J. Comput. Phys. 227, 6944–6966 (2008).
Brochard, G., Dumont, R., Lütjens, H. & Garbet, X. Linear stability of the ITER 15 MA scenario against the alpha fishbone. Nucl. Fusion 60, 086002 (2020).
Jaeger, E., Berry, L., D’Azevedo, E., Batchelor, D. & Carter, M. All-orders spectral calculation of radio-frequency heating in two-dimensional toroidal plasmas. Phys. Plasmas 8, 1573–1583 (2001).
Bertelli, N., Shiraiwa, S., Helou, W., Milanesio, D. & Tierens, W. Benchmark between antenna code TOPICA, RAPLICASOL and Petra-M for the ICRH ITER antenna. AIP Conf. Proc. 2984, 060006 (2023).
Jucker, M. Self-Consistent ICRH Distribution Functions and Equilibria in Magnetically Confined Plasmas. PhD thesis, EPFL (2010).
Milanesio, D., Meneghini, O., Lancellotti, V., Maggiora, R. & Vecchi, G. A multi-cavity approach for enhanced efficiency in TOPICA RF antenna code. Nucl. Fusion 49, 115019 (2009).
Mosher, S. W. et al. ADVANTG — An Automated Variance Reduction Parameter Generator (Rev. 1). Report No. ORNL/TM-2013/416 (Oak Ridge National Laboratory, 2015).
Wareing, T., McGhee, J. & Morel, J. ATTILA. A 3D unstructured tetrahedral-mesh Sn code. (OECD, 2025).
Wagner, J., Mosher, S., Evans, T., Peplow, D. & Turner, J. Hybrid and parallel domain-decomposition methods development to enable Monte Carlo for reactor analyses. Prog. Nucl. Sci. Techol. 2, 815 (2011).
Agostinelli, S. et al. Geant4 — a simulation toolkit. Nucl. Instrum. Methods Phys. Res. A 506, 250–303 (2003).
Werner, C. J. et al. MCNP Version 6.2 Release Notes. Report No. LA-UR-18-20808 (US Department of Energy, 2018).
Romano, P. K. et al. OpenMC: A state-of-the-art Monte Carlo code for research and development. Ann. Nucl. Energy 82, 90–97 (2015).
Zheng, Y. et al. An improved on-the-fly global variance reduction technique by automatically updating weight window values for Monte Carlo shielding calculation. Fusion Eng. Des. 147, 111238 (2019).
Leppänen, J., Pusa, M., Viitanen, T., Valtavirta, V. & Kaltiaisenaho, T. The Serpent Monte Carlo code: status, development and applications in 2013. Ann. Nucl. Energy 82, 142–150 (2015).
Diop, C. et al. TRIPOLI-4: a 3D continuous-energy Monte Carlo transport code. Trans. Am. Nucl. Soc. 95, 661 (2006).
Ansys Mechanical https://www.ansys.com/products/structures/ansys-mechanical (ANSYS Inc., 2024).
Mohr, S., Eixarch, M., Amsler, M., Mantsinen, M. J. & Genovese, L. Linear scaling DFT calculations for large tungsten systems using an optimized local basis. Nucl. Mater. Energy 15, 64–70 (2018).
Dovesi, R. et al. Quantum-mechanical condensed matter simulations with CRYSTAL. Wiley Interdiscip. Rev. Comput. Mol. Sci. 8, e1360 (2018).
Thompson, A. P. et al. LAMMPS — a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales. Comput. Phys. Commun. 271, 108171 (2022).
Santos-Güemes, R., Ortiz, C. J. & Segurado, J. An FFT based approach to account for elastic interactions in OkMC: application to dislocation loops in iron. J. Nucl. Mater. 594, 155020 (2024).
Ghaly, M., Nordlund, K. & Averback, R. Molecular dynamics investigations of surface damage produced by kiloelectronvolt self-bombardment of solids. Phil. Mag. A 79, 795–820 (1999).
Schleife, A., Draeger, E. W., Anisimov, V. M., Correa, A. A. & Kanai, Y. Quantum dynamics simulation of electrons in materials on high-performance computers. Comput. Sci. Eng. 16, 54–60 (2014).
Giannozzi, P. et al. Advanced capabilities for materials modelling with Quantum ESPRESSO. J. Phys. Condens. Matter 29, 465901 (2017).
Kresse, G. & Furthmüller, J. Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set. Phys. Rev. B 54, 11169–11186 (1996).
Sanz, J. ACAB Activation Code for Fusion Applications: User’s Manual V5.0. UCRLMA-143238 (Lawrence Livermore National Laboratory, 2000).
García, M., Catalán, J., García, R. & Sanz, J. in Challenges for Coolants in Fast Neutron Spectrum Systems 132–139 (IAEA, 2020).
Ansys Fluent https://www.ansys.com/products/fluids/ansys-fluent (ANSYS Inc., 2024).
Sauvan, P. et al. D1SUNED system for the determination of decay photon related quantities. Fusion Eng. Des. 151, 111399 (2020).
Varoutis, S. et al. Numerical simulation of neutral gas dynamics in the W7-X sub-divertor. Nucl. Fusion 64, 076011 (2024).
Delaporte-Mathurin, R. et al. FESTIM: an open-source code for hydrogen transport simulations. Int. J. Hydrogen Energy 63, 786–802 (2024).
Weller, H. G., Tabor, G., Jasak, H. & Fureby, C. A tensorial approach to computational continuum mechanics using object-oriented techniques. Comput. Phys. 12, 620–631 (1998).
Luo, X. & Day, C. 3D Monte Carlo vacuum modeling of the neutral beam injection system of ITER. Fusion Eng. Des. 85, 1446–1450 (2010).
STAR-CCM+ https://www.plm.automation.siemens.com/global/en/products/simcenter/STAR-CCM.html (Siemens, 2025).
Comsol Multiphysics version 6.0 (COMSOL AB, 2022).
Hecht, F. New development in freefem++. J. Numer. Math. 20, 251–266 (2012).
Caravello, M., Aimetta, A., Abrate, N., Dulla, S. & Froio, A. An OpenFOAM solver for multiphysics modeling of fusion reactor design: the nemoFoam code. Nucl. Mater. Energy 40, 101693 (2024).
Acknowledgements
This work has been carried out within the framework of the EUROfusion Consortium, funded by the European Union via the Euratom Research and Training Programme (Grant Agreement No. 101052200 — EUROfusion). Views and opinions expressed are, however, those of the author(s) only and do not necessarily reflect those of the European Union or the European Commission. Neither the European Union nor the European Commission can be held responsible for them. In addition, the work has been partly co-financed by grant PID2023-148038OB-I00 funded by MICIU/AEI/10.13039/501100011033/ and by the Departament de Recerca i Universitats de la Generalitat de Catalunya with code 2021 SGR 00908. The authors thank F. Cipolletta, D. Gallart, J. J. Gutierrez Moreno, J. J. Labarta Mancho and A. Soba for discussions.
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Garcia-Gasulla, M., Mantsinen, M.J. Challenges and opportunities in exascale fusion simulations. Nat Rev Phys 7, 355–364 (2025). https://doi.org/10.1038/s42254-025-00830-8
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DOI: https://doi.org/10.1038/s42254-025-00830-8