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Container technologies such as Docker have become a crucial component of manysoftware industry practices especially those pertaining to reproducibility andportability. The containerization philosophy has influenced the scientificcomputing community, which has begun to adopt - and even develop - containertechnologies (such as Singularity). Leveraging containers for scientificsoftware often poses challenges distinct from those encountered in industry,and requires different methodologies. This is especially true for HPC. With anincreasing number of options for HPC in the cloud (including NSF-funded cloudprojects), there is strong motivation to seek solutions that provideflexibility to develop and deploy scientific software on a variety ofcomputational infrastructures in a portable and reproducible way. Theflexibility offered by cloud services enables virtual HPC clusters that scaleon-demand, and the Cyberinfrastructure Resource Integration team in the XSEDEproject has developed a set of tools which provides scalable infrastructure inthe cloud. We now present a solution which uses the Nix package manager in anMPI-capable Docker container that is converted to Singularity. It providesconsistent installations, dependencies, and environments in each image that arereproducible and portable across scientific computing infrastructures. Wedemonstrate the utility of these containers with cluster benchmark runs in aself-scaling virtual cluster using the Slurm scheduler deployed in theJetstream and Aristotle Red Cloud OpenStack clouds. We conclude this techniqueis useful as a template for scientific software application containers to beused in the XSEDE compute environment, other Singularity HPC environments, andcloud computing environments.

Self-Scaling Clusters and Reproducible Containers to Enable Scientific Computing