# Reproducible Workflow on a Public Cloud for Computational Fluid Dynamics

## TL;DR

This paper presents a cloud-based workflow using Docker and Azure to enhance transparency and reproducibility in computational fluid dynamics research, including benchmarks and practical insights from two years of use.

## Contribution

It introduces a comprehensive reproducibility package and demonstrates the viability of Azure cloud for HPC and GPU-accelerated CFD simulations.

## Key findings

- Azure HPC nodes are comparable to traditional clusters.
- Cloud computing can effectively run GPU-based CFD simulations.
- The workflow improves reproducibility and transparency in computational research.

## Abstract

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.