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At the same time, the hardware for high-performance computing is becoming increasingly powerful. Geneva is a framework for parallel optimization of large-scale problems with highly nonlinear quality surfaces in grid and cloud environments. To harness the immense computing power of high-performance computing clusters, we have developed a new networking component for Geneva\u2014the so-called MPI Consumer\u2014which makes Geneva suitable for HPC. Geneva is most prominent for its evolutionary algorithm, which requires repeatedly evaluating a user-defined cost function. The MPI Consumer parallelizes the computation of the candidate solutions\u2019 cost functions by sending them to remote cluster nodes. By using an advanced multithreading mechanism on the master node and by using asynchronous requests on the worker nodes, the MPI Consumer is highly scalable. Additionally, it provides fault tolerance, which is usually not the case for MPI programs but becomes increasingly important for HPC. Moreover, the MPI Consumer provides a framework for the intuitive implementation of fine-grained parallelization of the cost function. Since the MPI Consumer conforms to the standard paradigm of HPC programs, it vastly improves Geneva\u2019s user-friendliness on HPC clusters. This article gives insight into Geneva\u2019s general system architecture and the system design of the MPI Consumer as well as the underlying concepts. Geneva\u2014including the novel MPI Consumer\u2014is publicly available as an open source project on GitHub (\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/gemfony\/geneva\">https:\/\/github.com\/gemfony\/geneva<\/jats:ext-link>\n                    ) and is currently used for fundamental physics research at GSI in Darmstadt, Germany.\n                  <\/jats:p>","DOI":"10.1007\/s41781-023-00098-6","type":"journal-article","created":{"date-parts":[[2023,4,21]],"date-time":"2023-04-21T13:30:50Z","timestamp":1682083850000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Parametric Optimization on HPC Clusters with Geneva"],"prefix":"10.1007","volume":"7","author":[{"given":"Jonas","family":"We\u00dfner","sequence":"first","affiliation":[]},{"given":"R\u00fcdiger","family":"Berlich","sequence":"additional","affiliation":[]},{"given":"Kilian","family":"Schwarz","sequence":"additional","affiliation":[]},{"given":"Matthias F. 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