# Application-Driven Exascale: The JUPITER Benchmark Suite

arXiv: https://arxiv.org/abs/2408.17211 · HTML: https://mlsanity.com/paper/2408.17211/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Aug 2024.

## Built on by (most cited first, 3 of 3)

- [Performance and Power: Systematic Evaluation of AI Workloads on Accelerators with CARAML](https://mlsanity.com/paper/2409.12994/) (2024)
- [Introducing Milabench: Benchmarking Accelerators for AI](https://mlsanity.com/paper/2411.11940/) (2024)
- [4D Parallelism Unlocks Exascale Bayesian Neural Networks for High-Fidelity Atmospheric Modeling](https://mlsanity.com/paper/2609.12815/) (2026)

## Abstract (arXiv)

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 https://github.com/FZJ-JSC/jubench.