Advanced Research Computing Containers for AI Workloads
Event Type
Student Paper
TimeTuesday, July 3012pm - 12:30pm
LocationCrystal C
DescriptionContainerization 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 tradeoffs
of deploying applications using a container based model. The
authors are not aware of any published studies evaluating the tradeoffs
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.