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ARC Containers for AI Workloads: Singularity Performance Overhead

Containerization has taken the software world by storm. Deployment complications, like requiring elevated (i.e. "root") permissions to run, have slowed the adoption of containers in shared advanced research computing (ARC) environments. Singularity is a containerization approach that is designed for ARC in shared high performance computing (HPC) clusters. With the creation of the Singularity, there is finally a viable scientific container solution. However very few papers have looked at the performance trade-offs of deploying applications using a container based model. The authors are not aware of any published studies evaluating the trade-offs of the deployment models with complex Artificial Intelligence (AI) workloads. Without detailed evaluations of the performance trade-offs scientists and engineers are unable to make an informed decision on deployment model for time sensitive training or low power inference. Furthering previous research in this area and using emerging community developed benchmarks, we examine performance trade-offs of running AI workloads in a containerized Singularity environment.