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With the fast development of supercomputers, energy consumption by large scale computer systems has become a major concern. How to reduce energy consumption is now a critical issue in designing high-performance computing systems. Moreover, reducing energy consumption for high-performance computing can bring various benefits such as, reduce monetary operating costs, increase system reliability, and reduction of environmental impacts. Therefore, in this paper we address the problem of scheduling precedence-constrained parallel applications on heterogeneous scalable computing systems with the objectives of minimizing finish time and reduce energy consumption. We provide a scheduling algorithm based on the best-effort idea that adopts dynamic voltage scaling (DVS) to reduce energy consumption. That is, the algorithm firstly looks for nearoptimal solutions employing a list-based scheduling algorithm to find the minimum finish time (besteffort). Then, a fast random local search algorithm that exploits voltage scaling is used to reduce the energy consumption of the generated schedule without any performance degradation. Simulation results on structured graphs representing real-world applications emphasize the interest of the proposed approach. |
