How to use Kubeflow and the MPI Operator on OpenShift

This repository provides an example of using Kubeflow and its MPI operator on top of OpenShift. The examples specifically target running OpenFOAM CFD simulations with CPUs and GPUs. It uses OpenShift Data Foundation for providing RWX storage.


The growing adoption of Kubernetes provides a new opportunity to shed legacy HPC infrastructures. Kubernetes is effectively a general purpose scheduling system for containers. As many MPI-based workloads are already written on Linux, they can be easily containerized. The Kubeflow project has an early-stage operator that handles MPI applications.

OpenFOAM is an application suite used for computational fluid dynamics (CFD) analysis. It is capable of processing large jobs in parallel using MPI. These jobs frequently involve large numbers of processors (CPUs). For an organization with a large Kubernetes cluster at its disposal, making use of these large processor pools to perform MPI jobs in their spare time seems logical. Further, in cloud-based environments like AWS (where this example is designed to run), organizations can make use of the autoscaling features inside Kubernetes to simply create the capacity required to fulfill the MPI job’s requirements.

Deeper descriptions of MPI, workers, processors, and etc. is outside of the scope of this example. Knowledge of CFD and OpenFOAM is also outside of the scope of this example. Some understanding of both MPI and OpenFOAM is assumed, but not required. A solid grasp of Kubernetes and, to a degree, OpenShift, is assumed.

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