Modeling and evaluating energy-performance efficiency of parallel processing on multicore based power aware systems

In energy efficient high end computing, a typical problem is to find an energy-performance efficient resource allocation for computing a given workload. An analytical solution to this problem includes two steps: first estimating the performances and energy costs for the workload running with various resource allocations, and second searching the allocation space to identify the optimal allocation according to an energy-performance efficiency measure. In this paper, we develop analytical models to approximate performance and energy cost for scientific workloads on multicore based power aware systems. The performance models extend Amdahl´s law and power-aware speedup model to the context of multicore-based power aware computing. The power and energy models describe the power effects of resource allocation and workload characteristics. As a proof of concept, we show model parameter derivation and model validation using performance, power, and energy profiles collected on a prototype multicore based power aware cluster.