Energy Consumption Reduction with DVFS for Message Passing Iterative Applications on Heterogeneous Architectures

Abstract

Computing platforms are 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 reduces the frequency of a CPU to lower its energy consumption. 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 heterogeneous platforms (heterogeneous CPUs) 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 iterative application. The algorithm has a small overhead and works without training or profiling. It uses a new energy model for message passing iterative applications running on a heterogeneous platform. The proposed algorithm is evaluated on the SimGrid simulator while running the NAS parallel benchmarks. The experiments show that it reduces the energy consumption by up to 34% while limiting the performance degradation as much as possible. Finally, the algorithm is compared to an existing method, the comparison results show that it outperforms the latter, on average it saves 4% more energy while keeping the same performance.