HPDC '03 Proceedings of the 12th IEEE International Symposium on High Performance Distributed Computing
A Case For Grid Computing On Virtual Machines
ICDCS '03 Proceedings of the 23rd International Conference on Distributed Computing Systems
Executing and visualizing high energy physics simulations with grid technologies
ISPDC'03 Proceedings of the Second international conference on Parallel and distributed computing
Xen and the Art of Cluster Scheduling
VTDC '06 Proceedings of the 2nd International Workshop on Virtualization Technology in Distributed Computing
Scientific workflows and clouds
Crossroads - Plugging Into the Cloud
International Journal of Advanced Media and Communication
Performance evaluation of OpenMP applications on virtualized multicore machines
IWOMP'11 Proceedings of the 7th international conference on OpenMP in the Petascale era
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The simulations used in the field of high energy physics are compute intensive and exhibit a high level of data parallelism. These features make such simulations ideal candidates for Grid computing. We are taking as an example the GEANT4 detector simulation used for physics studies within the ATLAS experiment at CERN. One key issue in Grid computing is that of network and system security, which can potentially inhibit the wide spread use of such simulations. Virtualization provides a feasible solution because it allows the creation of virtual compute nodes in both local and remote compute clusters, thus providing an insulating layer which can play an important role in satisfying the security concerns of all parties involved. However, it has performance implications. This study provides quantitative estimates of the virtualization and hyper-threading overhead for GEANT on commodity clusters. Results show that virtualization has less than 15% run-time overhead, and that the best run time (with the non-SMP licence of ESX VMware) is achieved by using one virtual machine per CPU. We also observe that hyper-threading does not provide an advantage in this application. Finally, the effect of virtualization on run-time, throughput, mean response time and utilization is estimated using simulations.