Name
Abstract | ||
|---|---|---|
Cloud computing is emerging as an alternative computing platform to bridge the gap between scientists' growing computational demands and their computing capabilities. A scientist who wants to run HPC applications can obtain massive computing resources 'in the cloud' quickly (in minutes), as opposed to days or weeks it normally takes under traditional business processes. Due to the popularity of Amazon EC2, most HPC-in-the-cloud research has been conducted using EC2 as a target platform. Previous work has not investigated how results might depend upon the cloud platform used. In this paper, we extend previous research to three public cloud computing platforms. In addition to running classical benchmarks, we also port a 'full-size' NASA climate prediction application into the cloud, and compare our results with that from dedicated HPC systems. Our results show that 1) virtualization technology, which is widely used by cloud computing, adds little performance overhead; 2) most current public clouds are not designed for running scientific applications primarily due to their poor networking capabilities. However, a cloud with moderately better network (vs. EC2) will deliver a significant performance improvement. Our observations will help to quantify the improvement of using fast networks for running HPC-in-the-cloud, and indicate a promising trend of HPC capability in future private science clouds. We also discuss techniques that will help scientists to best utilize public cloud platforms despite current deficiencies. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1145/1851476.1851535 | HPDC |
Keywords | Field | DocType |
public cloud computing platform,cloud platform,computing capability,alternative computing platform,massive computing resource,public cloud platform,current public cloud,hpc application,cloud computing,amazon ec2,future private science cloud,case study,business process,high performance computing | Virtualization,Unconventional computing,Business process,Supercomputer,Computer science,Real-time computing,Utility computing,Cloud testing,Performance improvement,Distributed computing,Cloud computing | Conference |
Citations | PageRank | References |
80 | 3.45 | 10 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qi-Ming He | 1 | 230 | 34.21 |
| Shujia Zhou | 2 | 216 | 17.50 |
| Ben Kobler | 3 | 133 | 16.27 |
| Dan Duffy | 4 | 80 | 3.45 |
| Tom McGlynn | 5 | 95 | 4.81 |