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

Lan-Da Van, Tao Wang, Sing-Jia Tzeng, T. Jung
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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.
一种在多核CPU上并行化FastICA算法的计算感知TPL利用程序
独立分量分析是一种广泛使用的机器学习技术,用于将混合信号分离成统计上独立的分量。本研究提出一种计算感知(CA)任务并行库(TPL)利用程序,在多核CPU上并行化快速独立成分分析(FastICA)算法。提出的CA方法通过在多核CPU上研究复杂计算的执行时间,将复杂计算与简单计算分离开来。TPL用于复杂的计算,但不用于简单的计算。与没有TPL的程序相比,本文提出的CA程序分解8路和32路人为混合信号的执行时间分别减少了34.88%和43.01%。与完全并行化的TPL程序相比,所提出的CA程序分解8路和32路人为混合信号的执行时间分别减少了10.04%和0.93%。使用CA TPL对12通道脑电图信号进行分解,分解时间比不使用CA TPL减少48.27%。与使用TPL的完全并行程序相比,所提出的CA过程的执行时间减少了15.12%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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