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Recent accelerators such as GPUs achieve better cost-performance and watt-performance ratio, while the range of their application is more limited than general CPUs. Thus heterogeneous clusters and supercomputers equipped both with accelerators and general CPUs are becoming popular, such as LANL's Roadrunner and our own TSUBAME supercomputer. Under the assumption that many applications will run both on CPUs and accelerators but with varying speed and power consumption characteristics, we propose a task scheduling scheme that optimize overall energy consumption of the system. We model task scheduling in terms of the scheduling makespan and energy to be consumed for each scheduling decision. We define acceleration factor to normalize the effect of acceleration per each task. The proposed scheme attempts to improve energy efficiency by effectively adjusting the schedule based on the acceleration factor. Although in the paper we adopted the popular EDP (Energy-Delay Product) as the optimization metric, our scheme is agnostic on the optimization function. Simulation studies on various sets of tasks with mixed acceleration factors, the overall makespan closely matched the theoretical optimal, while the energy consumption was reduced up to 13.8%.