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Evaluating the Impact of Full Virtualized High- Performance Computing Platform on Large Scale Scientific Data using Quantum Espresso
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 177 - Number 37 |
| Year of Publication: 2020 |
| Authors: Michael Ametepe Kattah, Dominic Asamoah, Frimpong Twum |
10.5120/ijca2020919856
|
Michael Ametepe Kattah, Dominic Asamoah, Frimpong Twum . Evaluating the Impact of Full Virtualized High- Performance Computing Platform on Large Scale Scientific Data using Quantum Espresso. International Journal of Computer Applications. 177, 37 ( Feb 2020), 10-14. DOI=10.5120/ijca2020919856
Abstract
High Performance Computing (HPC) applications are becoming vital in scientific research for analyzing large scale scientific data, but there is inadequate knowledge on the impact of a fully virtualized HPC cluster on these applications when they are used to analyzed large scientific data. The main purpose of this research is to carry out a comparative experiment on a virtual HPC cluster and the traditional HPC cluster by executing a benchmarking tool called para-speedup with an input file on both clusters using Quantum Espresso (QE) as an HPC application to determine the impact on the clusters. The research focuses on Central Processing Unit (CPU) utilization, turnaround time of jobs run on the cluster, memory and input/output (I/O) operations. The virtual cluster was setup using VMWare ESXi 5.5.0 as hypervisor of choice and ROCKS was installed on the cluster as an HPC platform of choice. During the experiment, it was observed that the job was not memory and I/O intensive on both clusters, so there was little to discuss on these metrics but generally it was observed that running job using HPC applications like QE on a fully virtualized HPC cluster to analyze large scale scientific data has a negative performance impact on the completion of the job as compared to the traditional cluster.
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