JUPITER Publications – Results from Research on the JUPITER Exascale Supercomputer
JUPITER enables groundbreaking applications of artificial intelligence and large-scale scientific simulations. This page highlights key scientific findings achieved with JUPITER. Some of the first results presented here are from projects of the JUPITER Research and Early Access Programme (JUREAP).
Research findings are continually being added as they are published.
Hierarchical network of thermal plumes and their dynamics in turbulent Rayleigh–Bénard convection (2025)
Key findings: The study shows that thermal convection is organised in a bottom-up hierarchy. Tiny thermal plumes, the basic “quanta” of convection, continually merge, disappear, and reform to create large-scale flow patterns – similar to how particles clump together in multiphase systems. Heat transport thus arises from local and highly fluctuating processes, challenging the traditional view that it stems from one global instability of the fluid layer near the wall.
Role of supercomputing: Capturing the fine-scale plume networks across extreme ranges of turbulence requires massive, high-resolution simulations. Only supercomputers like JUPITER provide the power to run these simulations at the necessary scales and levels of detail.
Shevkar, P. P., Samuel, R. J., Zinchenko, G., Bode, M., Schumacher, J., & Sreenivasan, K. R. (2025). Hierarchical network of thermal plumes and their dynamics in turbulent Rayleigh–Bénard convection. Proceedings of the National Academy of Sciences of the United States of America (PNAS), 122(32), e2502972122. https://doi.org/10.1073/pnas.2502972122
Enabling Ginkgo as Numerics Backend in nekRS Employing A Loosely-Coupled Configuration File Concept (2025 – in press)
Key findings: This paper presents an improved workflow for the nekRS simulation software to access Ginkgo's high performance numerics when running on the JUPITER supercomputer. The workflow builds on a generic software coupler and a configuration file that allows scientists to choose among a wide variety of numerical methods optimised for JUPITER's Grace Hopper architecture in between production runs without time-consuming software recompilation. For any given problem, this workflow allows to quickly identify a suitable numerical method.
Impact on science: The new workflow accelerates numerics-based simulations on JUPITER. Domain scientists can complete their production runs faster and achieve greater scientific output.
Tsai, Y.-H. M., Bode, M., & Anzt, H. (2025). Enabling Ginkgo as numerics backend in nekRS employing a loosely-coupled configuration file concept. Procedia Computer Science.
Application-Driven Exascale: The JUPITER Benchmark Suite (2024)
Key findings: Benchmarks play a vital role in the design of modern HPC systems, as they determine the critical characteristics of system components. To ensure high usability and broad adoption of new installations, benchmark suites must go beyond synthetic workloads and incorporate real applications that reflect actual user needs. The JUPITER Benchmark Suite has been developed to fulfill that requirement.
Impact on HPC procurement: The JUPITER Benchmark Suite enables an objective evaluation of system components, ensuring they are well-suited for real scientific use cases across a broad spectrum of research domains.
Herten, A., Achilles, S., Álvarez, D., Badwaik, J., Behle, E., Bode, M., Breuer, T., Caviedes‑Voullième, D., Cherti, M., Dabah, A., El Sayed Mohamed, S., Frings, W., Gonzalez‑Nicolas, A., Gregory, E. B., Haghighi Mood, K., Hater, T., Jitsev, J., John, C. M., Meinke, J. H., Meyer, C. I., Mezentsev, P., Mirus, J.‑O., Nassyr, S., Penke, C., Römmer, M., Sinha, U., von St. Vieth, B., Stein, O., Suarez, E., Willsch, D., & Zhukov, I. (2024). Application‑Driven Exascale: The JUPITER Benchmark Suite. SC24: International Conference for High Performance Computing, Networking, Storage and Analysis, Atlanta, GA, USA, 2024, 1-45. https://doi.ieeecomputersociety.org/10.1109/SC41406.2024.00038