By means of the availability of mechanisms such as Dynamic Voltage and Frequency
Scaling (DVFS) and heterogeneous architectures including processors with different
power consumption profiles, it is possible to devise scheduling algorithms aware of
both runtime and energy consumption in parallel programs. In this paper, we propose and evaluate a multi-objective (more specifically, a bi-objective) approach to
distribute the workload among the processing cores in a given heterogeneous parallel CPU-GPU architecture. The aim of this distribution may be either to save energy
without increasing the running time or to reach a trade-off among time and energy
consumption. The parallel programs considered here are master-worker evolutionary algorithms where the evaluation of the fitness function for the individuals in the
population demands the most part of the computing time. As many useful bioinformatics and data mining applications exhibit this kind of parallel profile, the proposed
energy-aware approach for workload scheduling could be frequently applied.Spanish Ministerio de Economía y Competitividad under grant TIN2015-67020-PERDF fun
