Energy aware performance study for a class of computationally intensive Monte Carlo algorithms

The latest developments in the domain of HPC have lead to the deployment of complex extreme-scale systems, based on diverse computing devices (CPU, GPU, accelerators) thus posing the question of scalability in the light not only of parallel efficiency, but also in terms of energy efficiency. In this paper we propose a new metrics for energy aware performance estimation based on our experience and the analysis of the existing metrics. We study the performance of computationally intensive Monte Carlo applications deployed on heterogeneous HPC systems with focus on energy efficiency and equipment costs. We compare the energy aware performance results of CPU and GPU variants of the tested algorithms with respect to the introduced measures and metrics. The results of our study demonstrate the importance of taking into account not only scalability of the HPC applications but also energy efficiency and equipment cost. They also show how to optimize the selection of CPU computing or computing with GPGPUs. The results can be used by application developers/users and also by resource providers.