Authors:
Fan Ding
1
;
Dieter an Mey
2
;
Sandra Wienke
2
;
Ruisheng Zhang
1
and
Lian Li
1
Affiliations:
1
Lanzhou University, China
;
2
RWTH Aachen University, Germany
Keyword(s):
Azure Cloud, MPI, HPC, Azure HPC Scheduler, SMEs
Related
Ontology
Subjects/Areas/Topics:
Cloud Computing
;
Development Methods for Cloud Applications
;
Platforms and Applications
Abstract:
With the advance of high-performance computing (HPC), more and more scientific applications which cannot be satisfied by on-premises compute power need large scale of computing resources, especially for small and medium-sized enterprises (SMEs). Emerging cloud computing offerings promise to provide us with enormous on-demand computing power. Many cloud platforms have been developed to provide users with various kinds of computer and storage resources. The user only needs to pay for the required resources and does not need to struggle with the underlying configuration of the operation system. But it is not always convenient for a user to migrate on-premises applications to these cloud platforms, which is especially true for an HPC application. In this paper, we proposed an HPC application deployment model based on the Windows Azure cloud platform, and developed an MPI application case on Azure.