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Cloud HPC strategies and performance for FEM
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing.
2016 (English)Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

High precision results for large scientific problems often require immense computational power, an investment which can be expensive and hard to access. Therefore companies are looking to the cloud, where providers are offering highly scalable on-demand computing power, virtual machines, over the internet. An example of this is the Amazon Elastic Compute Cloud (EC2), a service providing virtual machines, instead of direct access to physical computers. This enables a more efficient utilization of computer resources, resulting in affordable and effectively unlimited on-demand computing power. The downside with the cloud resources is heterogeneous and sub-optimal (due to sharing physical resources and virtualization overhead) performance. The findings show that the performance degradation of virtual machines differs depending on how much the resources are being shared with other users. 13-42% degradation for virtual machines are observed using a non-production grade system with the virtualization layer based on a non-optimized version of KVM hypervisor with resource overcommit. Running finite element method, FEM, simulations with COMSOL Multiphysics, a commercial FEM simulation software, on Amazon EC2 proved successful for large simulations, where the runtime for test problems is reduced using up to 16 virtual machines.

Place, publisher, year, edition, pages
2016. , 45 p.
UPTEC F, ISSN 1401-5757 ; 16005
National Category
Engineering and Technology
URN: urn:nbn:se:uu:diva-279033OAI: diva2:907419
External cooperation
ABB Corporate Research AB
Educational program
Master Programme in Engineering Physics
Available from: 2016-02-29 Created: 2016-02-28 Last updated: 2016-02-29Bibliographically approved

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