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Calculating the correlation in a sliding window is a common method ofstatistical evaluation of the interconnect between two sets of data. Andalthough the calculation of a single correlation coefficient is notresource-intensive and algorithmically complex, sequential computation in alarge number of windows on large data sets can take quite a long time. In thiscase, each value in the data, falling into different windows, will be processedmany times, increasing the complexity of the algorithm and the processing time.We took this fact into account and optimized the correlation calculation in thesliding window, reducing the number of operations in the overlapping area ofthe windows. In addition, we developed a parallel version of the optimizedalgorithm for the GPU architecture. Experimental studies have shown that for a7x7 correlation window sliding in one pixel increments, we were able toaccelerate the processing of an 12 MPixel image pixels on the GPU by about 60times compared to the serial version running on the CPU. The article presentsan optimized version of the algorithm, a scheme for its parallelization, aswell as the results of experimental studies.

Optimization of the n-dimensional sliding window inter-channel correlation algorithm for multi-core architecture