The current cosmological paradigm posits that at early times the distribution of matter in our universe had the statistics of a Gaussian process, and evolved primarily via gravitational collapse into the highly non-linear structures we see today. Both the numerical simulations and the observational catalogues produce big data that challenges the computational scaling of our analysis software. In this talk, I will present novel algorithms for the application of Gaussian processes over spherical domains using the Markov property, and a friends-of-friends clustering algorithm using spatial hashing.