HPC Scheduler as a Service and IHME's Mixed Workload Deployment

5th chunk of `content/en/blog/_posts/2017-08-00-Kubernetes-Meets-High-Performance.md`

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![](https://lh6.googleusercontent.com/wSBBl5d-YL4_UCYgvHpE_XzijtqftSi6PTHJLGfHr5nAxmTj945jQB-pMNIGLovWwKWGnEsPjCkCPrUMWZEs9UHnQPPDSWPEl-Gl76Yczd-Yn65pEE8mKC-Asj3zP5xyfZc-r2qU-YmmOyBhLQ)

From a client perspective, the HPC scheduler runs as a service deployed in Kubernetes pods, operating just as it would on a bare metal cluster. Navops Command provides additional scheduling features including things like resource reservation, run-time quotas, workload preemption and more. This environment works equally well for on-premise, cloud-based or hybrid deployments.

## Deploying mixed workloads at IHME

One client having success with mixed workloads is the Institute for Health Metrics & Evaluation (IHME), an independent health research center at the University of Washington. In support of their globally recognized Global Health Data Exchange (GHDx), IHME operates a significantly sized environment comprised of 500 nodes and 20,000 cores running a mix of analytic, HPC, and container-based applications on Kubernetes. [This case study](http://navops.io/ihme-case-study.html) describes IHME’s success hosting existing HPC workloads on a shared Kubernetes cluster using Navops Command.

Title: HPC Scheduler as a Service and IHME's Mixed Workload Deployment

Summary

The HPC scheduler operates as a service within Kubernetes pods, similar to its operation on bare metal. Navops Command offers extra scheduling features. IHME, an independent health research center, uses this approach successfully to manage a mix of analytic, HPC, and container-based applications on Kubernetes, as detailed in a case study.