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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.