  In a new effort to make our research transparent and reproducible by others,
we developed a workflow to run and share computational studies on the public
cloud Microsoft Azure. It uses Docker containers to create an image of the
application software stack. We also adopt several tools that facilitate
creating and managing virtual machines on compute nodes and submitting jobs to
these nodes. The configuration files for these tools are part of an expanded
"reproducibility package" that includes workflow definitions for cloud
computing, in addition to input files and instructions. This facilitates
re-creating the cloud environment to re-run the computations under the same
conditions. Although cloud providers have improved their offerings, many
researchers using high-performance computing (HPC) are still skeptical about
cloud computing. Thus, we ran benchmarks for tightly coupled applications to
confirm that the latest HPC nodes of Microsoft Azure are indeed a viable
alternative to traditional on-site HPC clusters. We also show that cloud
offerings are now adequate to complete computational fluid dynamics studies
with in-house research software that uses parallel computing with GPUs.
Finally, we share with the community what we have learned from nearly two years
of using Azure cloud to enhance transparency and reproducibility in our
computational simulations.
