  Converged computing brings together the best of both worlds for high
performance computing (HPC) and cloud-native communities. In fact, the economic
impact of cloud-computing, and need for portability, flexibility, and
manageability make it not important, but inevitable. Navigating this uncharted
territory requires not just innovation in the technology space, but also effort
toward collaboration and sharing of ideas. With these goals in mind, this work
first tackles the central component of running batch workflows, whether in
cloud or HPC: the workload manager. For cloud, Kubernetes has become the de
facto tool for this kind of batch orchestration. For HPC, the next-generation
HPC workload manager Flux Framework is analogous -- combining fully
hierarchical resource management and graph-based scheduling to support
intelligent scheduling and job management. Convergence of these managers would
mean the implementation of Flux inside of Kubernetes, allowing for hierarchical
resource management and scheduling that scales impressively without burdening
the Kubernetes scheduler itself. This paper introduces the Flux Operator -- an
on-demand HPC workload manager that is easily deployed in Kubernetes. The work
here highlights design decisions, mapping of components between environments,
experimental features, and shares the results of experiments that compare
performance with an equivalent operator in the space, the MPI Operator.
Finally, discussion closes with a review of challenges remaining, and hopes for
the future for improved technological innovation and collaboration.
