Optimizing the Trade-offs Between Cost and Performance in Scientific Computing

Satisfying the global throughput targets of scientific applications is an important challenge in High Performance Computing (HPC) systems. The main diffculty lies in the high number of parameters having an important impact on the overall system performance. These include the number of storage servers, features of communication links, and the number of CPU cores per node, among many others.In this paper we present a model that computes a cost-per-performance metric using different hardware configu-rations and focusing on scientific computing. The main goal of this approach is to balance the trade-off between cost and performance using different combinations of components for building the entire system. The main advantage of our approach is that we simulate the different configurations in a complex simulation platform. Therefore, it is not necessary to make an investment until the system computes the different alternatives and suggest good solutions. In order to achieve this goal, both the system's architecture and Map-Reduce applications are modeled. The proposed model has been evaluated by building complex systems in a simulated environment using the SIMCAN simulation platform.