A Computation-Aware TPL Utilization Procedure for Parallelizing the FastICA Algorithm on a Multi-Core CPU

Abstract

Independent Component Analysis is a widely used machine learning technique to separate mixed signals into statistically independent components. This study proposes a computation-aware (CA) Task Parallel Library (TPL) utilization procedure to parallelize the Fast Independent Component Analysis (FastICA) algorithm on a multi-core CPU. The proposed CA method separates the complex from simple computations by exploring their execution times on a multi-core CPU. TPL is used for complex calculations, but not for simple ones. In comparison to the program without the TPL, the proposed CA procedure reduces the execution time of decomposing 8- and 32-channel artificially mixed signals by 34.88% and 43.01%, respectively. The proposed CA procedure reduces the execution time of decomposing 8- and 32-channel artificially mixed signals by 10.04% and 0.93%, respectively, compared to the fully parallelized program with TPL. Using CA TPL, the decomposition of 12-channel electroencephalograms (EEG) signals take 48.27% less time than without it. The proposed CA procedure reduces execution time by 15.12% compared to the fully parallelized program with TPL.