Communication-energy-aware scheduling of task graphs

Minimizing the energy consumption of computing is essential to improve efficiency and enable further performance growth. A central point where to address this is in the scheduling of tasks onto multiprocessor systems. In many previous studies the focus of static scheduling algorithms has been on the energy consumption of the processors, where techniques like Dynamic Voltage and Frequency Scaling (DVFS) have been successfully utilized. Despite the large body of work, a notable gap exists: the energy consumed by data transfer, in particular by inter-processor communication, is neglected, even though it becomes more and more significant. To address this, we introduce a revised model that incorporates the energy costs related to task communication, combining it with the existing DVFS techniques and different static energy models. Using the proposed novel energy model, we adjust existing and propose new algorithms and study their performance as influenced by the newly introduced energy parameters. The algorithms cover both major categories, namely : list-scheduling and cluster-scheduling. Using a large set of task graphs of many different structures and sizes, and different parallel systems, our evaluation offers an extensive comparison of the algorithms and provides insights into their ability to conserve energy within the new model’s framework.