As observed in Figure of Lange et al., strong correlations are detected near the main diagonal, due to close backbone connections, as well as two other diagonals perpendicular to the main. As mentioned by Lange et al., these correlations represent tight hydrogen-bond contacts between beta strands β1 and β2, as well as between β3 and β4. A weaker correlation close to the main diagonal between residues 22 and 38 are caused by the less-tight-packing within the α-helix. The new signals that appeared due to the use of GC, but not linear correlation, include a connection between the α-helix and the β2 sheet -probably due to their close proximity. One interesting result of this analysis implies that, while linear correlations would detect correlations within secondary structure elements, GC showed the ability to detect correlations between different secondary structure elements, as highlighted by Lange et al. . We used the local alignment procedure for this analysis which, due to protein G's relatively small size, had little effect on the outputs, and very similar results to Lange et al. were generated, in spite of the fact that Lange et al. aligned the entire trajectory using a global least-squares fitting to all alpha carbons. Figure : Benchmarks of Netsci computed for sets of data points sampled from independent Gaussian distributions. For each platform, pairwise MI quantities for 10, 30 and 100 nodes were defined, each with independent Gaussian distributions relating each pair of nodes. MI values were computed pairwise for each distribution. The x-axes indicate the number of data points, for the set of all distributions, that were sampled and fed into the MI algorithm. The time per MI calculation is plotted on the y-axis, representing the time per individual MI calculation between each pair of nodes. For larger inputs, benchmarks seemed to show that the GPU algorithm consistently performed approximately three orders of magnitude faster than the CPU implementation using GTX1080 GPUs, and yet another order of magnitude of performance, at least, was gained by using RTX6000 Ada GPUs. Note that some data points overlap due to close similarities in the benchmarks.