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The two-point correlation function (2PCF) is the most widely used tool forquantifying the spatial distribution of galaxies. Since the distribution ofgalaxies is determined by galaxy formation physics as well as the underlyingcosmology, fitting an observed correlation function yields valuable insightsinto both. The calculation for a 2PCF involves computing pair-wise separationsand consequently, the computing time scales quadratically with the number ofgalaxies. The next-generation galaxy surveys are slated to observe manymillions of galaxies, and computing the 2PCF for such surveys would beprohibitively time-consuming. Additionally, modern modelling techniques requirethe 2PCF to be calculated thousands of times on simulated galaxy catalogues of{\em at least} equal size to the data and would be completely unfeasible forthe next generation surveys. Thus, calculating the 2PCF forms a substantialbottleneck in improving our understanding of the fundamental physics of theuniverse, and we need high-performance software to compute the correlationfunction. In this paper, we present Corrfunc --- a suite of highly optimised,OpenMP parallel clustering codes. The improved performance of Corrfunc arisesfrom both efficient algorithms as well as software design that suits theunderlying hardware of modern CPUs. Corrfunc can compute a wide range of 2-Dand 3-D correlation functions in either simulation (Cartesian) space or on-skycoordinates. Corrfunc runs efficiently in both single- and multi-threaded modesand can compute a typical 2-point projected correlation function ($w_p(r_p)$)for ~1 million galaxies within a few seconds on a single thread. Corrfunc isdesigned to be both user-friendly and fast and is publicly available athttps://github.com/manodeep/Corrfunc.

Corrfunc --- A Suite of Blazing Fast Correlation Functions on the CPU