Computational Fluid Dynamics (CFD) Analysis With Kubernetes, Kubeflow, And OpenFOAM

The presentation discusses the use of Kubernetes for running HPC workloads, specifically using OpenFOAM as an example. The speaker emphasizes the importance of tuning and optimizing the instance types and pods used for the job. They also mention potential future developments, such as using Nvidia GPUs and exploring new schedulers.

• Kubernetes can be used for running HPC workloads, but tuning and optimization are crucial
• OpenFOAM was used as an example of an MPI job that can be run on Kubernetes
• Future developments include using Nvidia GPUs and exploring new schedulers

The speaker mentions that they were able to successfully run OpenFOAM on Kubernetes, but encountered some issues with race conditions and MPI bootstrapping. They also discuss the importance of storage network performance when constantly reading and writing to storage.

Frequently, organizations build dedicated clusters for high-performance computing (HPC) workloads. These clusters may sit idle when there are no HPC jobs to run, which is a waste of expensive resources. Kubernetes clusters can run these workloads alongside all of the other applications typically deployed, which helps improve overall operations and drives higher utilization of resources. In this presentation, you will learn how the Kubeflow project and its MPI operator can be used to run computational fluid dynamics (CFD) jobs with OpenFOAM on top of a Kubernetes cluster.