Laura Gilbert

Performance implications of virtualization and hyper-threading on high energy physics applications in a grid environment

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.

Implications of virtualization on grids for high energy physics applications

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 widespread 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 license 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.