Profiling hemodynamic application for parallel computing in the cloud

Porting to the cloud large scientific applications designed and optimized for a standard HPC facility does not always pay off, mainly because of the implied communication pattern. By profiling the applications, researchers can build a performance model, which is able to give insights about how the application will perform on the cloud. To validate this approach, we use a hemodynamic application that embeds both heavy computations and extensive communications with several collective operations to exchange data across all processes. We expect that this case instance is a model for other applications. Our approach is based on profiling and modeling, and builds an analytical model for the communication pattern of the chosen hemodynamic application. We collect data both on an on-premise HPC system and on the Google Cloud infrastructure, and assess the prediction based on the analytic model. The outcome suggests that the prediction consistently underestimates the actual execution time, but correctly guess the scalability, thus allowing to strike a good balance between performance and costs. Finally, we introduce a figure of merit to assess cost vs performance between cloud and on-premise implementation, and validate a first version of such a model.