High Performance Computing Cloud - a Platform-as-a-Service Perspective

HPC applications are widely used in scientific and industrial research, data analytic and visualization, social behavioral studies etc. Most HPC applications require dedicated, available and highly customized resources and environments for computation since they exhibit intense resource utilization. These needs were traditionally provided by clusters and supercomputers which are difficult to setup, manage or operate. While majority of the HPC installations ensure good resource utilization, the reach of these is restricted to few who are members of a specific HPC community. Cloud computing is emerging as a latest computing technology. The on-demand nature of cloud has provoked interest to explore if cloud properties can be useful for HPC setups. This paper is a work in that direction. The prevalent public clouds have accessibility to many and have been explored by the HPC community too. The biggest deterrent identified on these computing platforms for HPC workloads is the virtualization layer used by the cloud systems for resource provisioning. In this paper we propose a Platform-as-a-Service model to build an HPC cloud setup. The key goals for the architecture design is to include features like on-demand provisioning both for hardware as well as HPC runtime environment for the cloud user and at the same time ensure that the HPC applications do not suffer virtualization overheads. The architecture builds the required HPC platform by providing dedicated node or a group of nodes booted with the desired HPC environment without the virtualization layer. Technologies like Wake-on-LAN and network booting are used to achieve this goal. Once the usage of these resources is relinquished, the same nodes are re-deployed for another HPC platform. Thus this architecture merges cloud properties with HPC platforms for delivering effective performance. We show the results of benchmarks used to evaluate performance difference between a virtualized and non-virtualized environment for this observation.