We have developed a new version of the high-performance Jülich universal quantum computer simulator (JUQCS-50) that leverages key features of the GH200 superchips as used in the JUPITER supercomputer, enabling simulations of a 50-qubit universal quantum computer for the first time. JUQCS-50 achieves this through three key innovations: (1) extending usable memory beyond GPU limits via high-bandwidth CPU–GPU interconnects and LPDDR5 memory; (2) adaptive data encoding to reduce memory footprint with acceptable trade-offs in precision and compute effort; and (3) an on-the-fly network traffic optimizer. These advances result in an 16.6-fold speedup over the previous 48-qubit record on the K computer.

We present the first-ever global simulation of the full Earth system at 1.25 km grid spacing, achieving highest time compression with an unseen number of degrees of freedom. Our model captures the flow of energy, water, and carbon through key components of the Earth system: atmosphere, ocean, and land. To achieve this landmark simulation, we harness the power of 8192 GPUs on Alps and 20480 GPUs on JUPITER, two of the world’s largest GH200 superchip installations. We use both the Grace CPUs and Hopper GPUs by carefully balancing Earth’s components in a heterogeneous setup and optimizing acceleration techniques available in ICON’s codebase. We show how separation of concerns can reduce the code complexity by half while increasing performance and portability. Our achieved time compression of 145.7 simulated days per day enables long studies including full interactions in the Earth system and even outperforms earlier atmosphere-only simulations at a similar resolution.

As systems and simulations grow in size and complexity, it is challenging to maintain efficient use of resources and avoid failures. In this scenario, monitoring becomes even more important and mandatory. This paper describes and discusses the benefits of the advanced monitoring and control tool JuMonC, which runs under user control alongside HPC simulations and provides valuable metrics via REST-API. In addition, plugin extensibility allows JuMonC to go a step further and provide computational steering of the simulation itself. To demonstrate the benefits and usability of JuMonC for large-scale simulations, two use cases are described employing nekRS and ICON on JURECA-DC, a supercomputer located at the Jülich Supercomputing Centre (JSC). Furthermore, a large-scale use case with nekRS on JSC’s flagship system JUWELS Booster is described. Finally, the interplay between JuMonC and LLview (a standard monitoring tool for HPC systems) is presented using a simple and secure JuMonC-LLview plugin, which collects performance metrics and enables their analysis in LLview. Overall, the portability and usefulness of JuMonC, together with its low performance impact, make it an important application for both current and future generations of exascale HPC systems. • Description of JuMonC, a novel tool for monitoring and control for simulations at scale. • Applications with nekRS and ICON. • Performance measurements up to 3200 GPUs. • Elaboration of the integration into LLview. • Discussion of the benefit for exascale workflows.

Benchmarks are essential in the design of modern HPC installations, as they define key aspects of system components. Beyond synthetic workloads, it is crucial to include real applications that represent user requirements into benchmark suites, to guarantee high usability and widespread adoption of a new system. Given the significant investments in leadership-class supercomputers of the exascale era, this is even more important and necessitates alignment with a vision of Open Science and reproducibility. In this work, we present the JUPITER Benchmark Suite, which incorporates 16 applications from various domains. It was designed for and used in the procurement of JUPITER, the first European exascale supercomputer. We identify requirements and challenges and outline the project and software infrastructure setup. We provide descriptions and scalability studies of selected applications and a set of key takeaways. The JUPITER Benchmark Suite is released as open source software with this work at github.com/FZJ-JSC/jubench

A scalable graphical method is presented for selecting datasets for the training phase of a classification task. For the heuristic, a clustering algorithm is required to get its computation cost in a reasonable proportion to the task itself. This step is succeeded by construction of an information graph of the underlying classification patterns using approximate nearest neighbor methods. Lastly, the information graph is used for reducing a given training set. The heuristic targets large datasets, since the primary goal is a significant reduction in training computation run-time without compromising prediction accuracy. Test results show that the approach significantly speedup the training task when compared against that of state-of the-art shrinking heuristics available in LIBSVM. Furthermore, the approaches closely follow or even outperform in prediction accuracy.