Optimizing the energy consumption of message passing applications with iterations executed over grids

In recent years, green computing has become an important topic in the supercomputing research domain. However, the computing platforms are still consuming more and more energy due to the increasing number of nodes composing them. To minimize the operating costs of these platforms many techniques have been used. Dynamic voltage and frequency scaling (DVFS) is one of them. It can be used to reduce the power consumption of the CPU while computing, by lowering its frequency. However, lowering the frequency of a CPU may increase the execution time of an application running on that processor. Therefore, the frequency that gives the best trade-off between the energy consumption and the performance of an application must be selected. In this paper, a new online frequency selecting algorithm for grids, composed of heterogeneous clusters, is presented. It selects the frequencies and tries to give the best trade-off between energy saving and performance degradation, for each node computing the message passing application with iterations. The algorithm has a small overhead and works without training or profiling. It uses a new energy model for message passing applications with iterations running on a grid. The proposed algorithm is evaluated on a real grid, the Grid'5000 platform, while running the NAS parallel benchmarks. The experiments on 16 nodes, distributed on three clusters, show that it reduces on average the energy consumption by 30% while the performance is on average only degraded by 3.2%. Finally, the algorithm is compared to an existing method. The comparison results show that it outperforms the latter in terms of energy consumption reduction and performance. (C) 2016 Elsevier B.V. All rights reserved.