Performance evaluation of parallel haemodynamic computations on heterogeneous clouds
| Autor: | Oleg Bystrov, E. Stupak, Arnas Kačeniauskas, Algirdas Maknickas, Vadimas Starikovičius, Ruslan Pacevič, Aleksandr Igumenov |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
bi-objective optimization problem
68M20 Speedup parallel computing 68M14 business.industry Computer science Computation General Engineering Cloud computing Energy consumption Virtualization computer.software_genre Multi-objective optimization haemodynamic flows parallel performance analysis Computational science energy consumption 65Y05 Overhead (computing) business computer Energy (signal processing) cloud computing |
| Zdroj: | Computing and informatics: Special issue: Providing computing solutions for exascale challenges, Bratislava : Slovak Academy of Sciences, Institute of Informatics, 2020, vol. 39, no. 4, p. 695-723 COMPUTING AND INFORMATICS; Vol. 39 No. 4 (2020): Computing and Informatics; 695–723 |
| ISSN: | 1335-9150 2585-8807 |
| Popis: | The article presents performance evaluation of parallel haemodynamic flow computations on heterogeneous resources of the OpenStack cloud infrastructure. The main focus is on the parallel performance analysis, energy consumption and virtualization overhead of the developed software service based on ANSYS Fluent platform, which runs on Docker containers of the private university cloud. The haemodynamic aortic valve flow described by incompressible Navier-Stokes equations is considered as a target application of the hosted cloud infrastructure. The parallel performance of the developed software service is assessed measuring the parallel speedup of computations carried out on virtualized heterogeneous resources. The performance measured on Docker containers is compared with that obtained by using the native hardware. The alternative solution algorithms are explored in terms of the parallel performance and power consumption. The investigation of a trade-off between the computing speed and the consumed energy is performed by using Pareto front analysis and a linear scalarization method. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|