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

Juan José Escobar1, Julio Ortega1, Antonio Francisco Díaz1

  • 1Department of Computer Architecture and Technology, CITIC, University of Granada , Granada, Spain .

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology
|June 30, 2018
PubMed
Summary
This summary is machine-generated.

Parallel processing on CPU-GPU platforms optimizes electroencephalogram feature selection. Distributing workloads across CPU and GPU cores reduces runtime and energy consumption, even with higher instantaneous power.

Keywords:
EEG classificationdynamic schedulingenergy-aware computingheterogeneous parallelismmultiobjective feature selectionsubpopulations

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Area of Science:

  • Bioinformatics
  • Biomedical Engineering
  • High-Performance Computing

Background:

  • CPU-GPU parallel implementations are crucial for computationally intensive tasks in bioinformatics and biomedical engineering.
  • Optimizing power-performance is essential for efficient execution of these applications.
  • Previous studies focused on runtime, but a comprehensive analysis of power-performance in heterogeneous environments is needed.

Purpose of the Study:

  • To analyze the power-performance behavior of a parallel evolutionary multiobjective electroencephalogram feature selection procedure on heterogeneous CPU-GPU platforms.
  • To evaluate the impact of different operating frequencies and workload distribution strategies on runtime and energy consumption.
  • To compare the proposed parallel approach with a master-worker model.

Main Methods:

  • Implementation of an evolutionary feature selection procedure using OpenMP for workload distribution and OpenCL for fitness evaluation.
  • Evaluation of various experimental configurations, including different operating frequencies and individual distribution methods.
  • Measurement of runtime and energy consumption for different parallel execution strategies.

Main Results:

  • Parallel processing on CPU-GPU platforms significantly reduces application runtime and overall energy consumption.
  • Lower operating frequencies do not always result in lower energy consumption due to the interplay of power and time.
  • Optimal workload distribution between CPU and GPU cores yields the best runtime and lowest energy usage compared to CPU-only execution.

Conclusions:

  • Heterogeneous CPU-GPU parallelization is an effective strategy for optimizing power-performance in complex bioinformatics and biomedical engineering applications.
  • Dynamic workload distribution across CPU and GPU cores is key to achieving both speedup and energy efficiency.
  • The findings challenge the assumption that lower frequencies universally reduce energy consumption in parallel computing scenarios.