Speeding-up Pearson Correlation Coefficient calculation on graphical processing units

Sample correlation coefficient is used widely for finding signal similarity in data processing, multimedia, pattern recognition and artificial intelligence applications. Pearson Correlation Coefficient is the most common measure for the correlation coefficient between discrete signals. Similarity search in huge pattern databases require a fast way of calculating the correlation coefficient between numerical vectors. In this paper, a parallel and efficient way of calculating Pearson Correlation Coefficient on commodity central processing units (CPUs) and graphical processing units (GPUs) is proposed. Different implementations for C++, OpenCL and CUDA are compared over a vast number of architectures and through a wide parameter range. Experimental results are given in a comparative manner and investigated in both software and hardware perspectives.