In this work, we develop a novel computational tool called "Netsci" that uses GPU-accelerated code in order to perform the k-nearest neighbors algorithm for estimating MI, and by extension, GC. We show that Netsci correctly predicts the MI for analytically verifiable systems, reproducing the results of non-GPUaccelerated (CPU) implementations. When compared to CPU implementations, Netsci can perform MI calculations many orders of magnitude faster, and is not subject to space constraints -Netsci is essentially limited only by hard constraints imposed by system hardware. Although we have prepared Netsci to be usable for a wide variety of data applications in any scientific field, we focus on applying Netsci to perform correlation analyses of real biomolecular systems of interest.