A distributed and energy-efficient KNN for EEG classification with dynamic money-saving policy in heterogeneous clusters

Abstract

Due to energy consumption’s increasing importance in recent years, energy-time efficiency is a highly relevant objective to address in High-Performance Computing (HPC) systems, where cost significantly impacts the tasks executed. Among these tasks, classification problems are considered due to their great computational complexity, which is sometimes aggravated when processing high-dimensional datasets. In addition, implementing efficient applications for high-performance systems is not an easy task since hardware must be considered to maximize performance, especially on heterogeneous platforms with multi-core CPUs. Thus, this article proposes an efficient distributed K-Nearest Neighbors (KNN) for Electroencephalogram (EEG) classification that uses minimum Redundancy Maximum Relevance (mRMR) as a feature selection technique to reduce the dimensionality of the dataset. The approach implements an energy policy that can stop or resume the execution of the program based on the cost per Megawatt. Since the procedure is based on the master-worker scheme, the performance of three different workload distributions is also analyzed to identify which one is more suitable according to the experimental conditions. The proposed approach outperforms the classification results obtained by previous works that use the same dataset. It achieves a speedup of 74.53 when running on a multi-node heterogeneous cluster, consuming only 13.38% of the energy consumed by the sequential version. Moreover, the results show that financial costs can be reduced when energy policy is activated and the importance of developing efficient methods, proving that energy-aware computing is necessary for sustainable computing.

Vampire: A smart energy meter for synchronous monitoring in a distributed computer system

“…Note that the standard deviations obtained from the power consumption in the execution of the Linpack (±9.42 and ±9.65 W) have a high value, corresponding to the fact that the load is not constant, since the power consumption depends on the instructions that are running at any moment (not all consume the same amount of power) and the specific hardware resources in use (number of active cores, for example). Among other real-world experiments, the Vampire have been used to measure the energy consumption of programs for EEG classification [141]. The dataset includes 178 EEG signals for training and another 178 for testing, each with 3600 features, reported by the BCI laboratory of the University of Essex and corresponds to Brain Computer Interface (BCI) signals [142].…”

“…Therefore, the KNN algorithm deals with a 3-class classification problem. KNN has been chosen for two reasons: the first, because it is the method that currently obtains the best precision with the databases discussed here [141]. Secondly, because when testing different workload distributions and input parameters, such as the number of neighbours (K), the energy consumption of the procedure could vary.…”