A Power–Performance Perspective to Multiobjective Electroencephalogram Feature Selection on Heterogeneous Parallel Platforms

Journal of computational biology: A journal of computational molecular cell biology

Juan JoséEscobar^1,2¹  JulioOrtega^2² 

[1] Address correspondence to:Juan José Escobar, MScDepartment of Computer Architecture and TechnologyCITICUniversity of GranadaCalle Periodista Rafael Gómez Montero, 2Granada 18014Spain^1;Department of Computer Architecture and Technology, CITIC, University of Granada, Granada, Spain^2

关键词: dynamic scheduling;    EEG classification;    energy-aware computing;    heterogeneous parallelism;    multiobjective feature selection;    subpopulations;   

DOI  :  10.1089/cmb.2018.0080

学科分类：生物科学（综合）

来源: Mary Ann Liebert, Inc. Publishers

【 摘 要 】

This article provides an insight on the power–performance issues related with the CPU-GPU (Central Processing Unit-Graphics Processing Unit) parallel implementations of problems that frequently appear in the context of applications on bioinformatics and biomedical engineering. More specifically, we analyze the power–performance behavior of an evolutionary parallel multiobjective electroencephalogram 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 article 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.

【 授权许可】

Unknown