Industrially relevant computational fluid dynamics simulations frequently require vast computational resources that are only available to governments, wealthy corporations, and wealthy institutions. Thus, in many contexts and realities, high-performance computing grids and cloud resources on demand should be evaluated as viable alternatives to conventional computing clusters. In this work, we present the analysis of the time-to-solution and cost of an entropy stable collocated discontinuous Galerkin (SSDC) compressible computational fluid dynamics framework on Ibex, the on-premises cluster at KAUST, and the Amazon Web Services Elastic Compute Cloud for complex compressible flows. SSDC is a prototype of the next generation computational fluid dynamics frameworks developed following the road map established by the NASA CFD vision 2030. We simulate complex flow problems using high-order accurate fully-discrete entropy stable algorithms. In terms of time-to-solution, the Amazon Elastic Compute Cloud delivers the best performance, with the Graviton2 processors based on the Arm architecture being the fastest. However, the results also indicate that the Ibex nodes based on the AMD Rome architecture deliver good performance, close to those observed for the Amazon Elastic Compute Cloud. Furthermore, we observed that computations performed on the Ibex on-premises cluster are currently less expensive than those performed in the cloud. Our findings could be used to develop guidelines for selecting high-performance computing cloud resources to simulate realistic fluid flow problems.
- Amazon Web Services Elastic Compute Cloud
- Cloud computing
- Compressible Navier–Stokes equations
- Fluid mechanics
- Fully-discrete entropy stable algorithms
ASJC Scopus subject areas
- Computer Science(all)