A Power-Performance Perspective to Multiobjective Electroencephalogram Feature Selection on Heterogeneous Parallel Platforms Escobar Pérez, Juan José Ortega Lopera, Julio Díaz García, Antonio Francisco González Peñalver, Jesús Damas Hermoso, Miguel Heterogeneous Parallelism Energy-aware Computing Dynamic Scheduling EEG Classification Multi-objective Feature Selection Subpopulations This paper provides an insight on the power-performance issues related with the CPU-GPU parallel implementations of problems that frequently appear in the context of applications on bioinformatics and biomedical engineering. More specifically, we analyse the power-performance behaviour of an evolutionary parallel multi-objective electroencephalogram (EEG) feature selection procedure that evolves subpopulations of solutions with time-demanding fitness evaluation. The procedure has been implemented in OpenMP to dynamically distribute either subpopulations or individuals among devices, and uses OpenCL to evaluate the fitness of the individuals. The development of parallel codes usually implies to maximize the code efficiency, thus optimizing the achieved speedups. To follow the same trend, this paper extends and provides a more complete analysis of our previous works about the power-performance characteristics in heterogeneous CPU-GPU platforms considering different operation frequencies and evolutionary parameters such as distribution of individuals, etc. This way, different experimental configurations of the proposed procedure have been evaluated and compared with respect to a master-worker approach, not only in runtime but also considering energy consumption. The experimental results show that lower operating frequencies does not necessarily mean lower energy consumptions since energy is the product of power and time. Thus, we have observed that parallel processing not only reduces the runtime but also the energy consumed by the application despite a higher instantaneous power. Particularly, the workload distribution among both CPU and GPU cores provides the best runtime and very low energy consumption compared with the values achieved by the same alternatives executed by only CPU threads. 2024-01-22T08:48:59Z 2024-01-22T08:48:59Z 2018-08-01 journal article Juan José Escobar, Julio Ortega, Antonio Francisco Díaz, Jesús González, and Miguel Damas. A Power-Performance Perspective to Multiobjective Electroencephalogram Feature Selection on Heterogeneous Parallel Platforms. Journal of Computational Biology. Aug 2018. 882-893. http://doi.org/10.1089/cmb.2018.0080 https://hdl.handle.net/10481/87041 10.1089/cmb.2018.0080 eng http://creativecommons.org/licenses/by-nc-nd/3.0/ open access Creative Commons Attribution-NonCommercial-NoDerivs 3.0 License Mary Ann Liebert, Inc.