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

Big Data challenges often require application of new data processing paradigms (like MapReduce), and corresponding software solutions (e. g. Hadoop). This trend causes a pressure on both cyber-infrastructure providers (to quickly integrate new services) and infrastructure users (to quickly learn to use new tools). In this paper we present the concept of DARIAH Generic Workspace for Big Data Processing in eHumanities which alleviates the aforementioned problems. It establishes a common integration layer, thus enables a quick integration of new services, and by providing unified interfaces, allows the users to start using new tools without learning their internal details. We describe the overall architecture and implementation details of the working prototype. The presented concept is generic enough to be applied in other emerging cyber-infrastructures for humanities.