GPU-PCC: A GPU Based Technique to Compute Pairwise Pearson's Correlation Coefficients for Big fMRI Data

TLDR: This paper proposes GPU-PCC, a GPU based algorithm based on vector dot product, which is able to compute pairwise Pearson's Correlation Coefficient while performing computation once for each pair without the need to do post-processing reordering of coefficients.

Abstract: Functional Magnetic Resonance Imaging (fMRI) is a non-invasive brain imaging technique for studying the brain's functional activities. Pearson's Correlation Coefficient is an important measure for capturing dynamic behaviors and functional connectivity between brain components. One bottleneck in computing Correlation Coefficients is the time it takes to process big fMRI data. In this paper, we propose GPU-PCC, a GPU based algorithm based on vector dot product, which is able to compute pairwise Pearson's Correlation Coefficients while performing computation once for each pair. Our method is able to compute Correlation Coefficients in an ordered fashion without the need to do post-processing reordering of coefficients. We evaluated GPU-PCC using synthetic and real fMRI data and compared it with sequential version of computing Correlation Coefficient on CPU and existing state-of-the-art GPU method. We show that our GPU-PCC runs 94.62x faster as compared to the CPU version and 4.28x faster than the existing GPU based technique on a real fMRI dataset of size 90k voxels. The implemented code is available as GPL license on GitHub portal of our lab at https://github.com/pcdslab/GPU-PCC.