Corrfunc --- A Suite of Blazing Fast Correlation Functions on the CPU
  The two-point correlation function (2PCF) is the most widely used tool for
quantifying the spatial distribution of galaxies. Since the distribution of
galaxies is determined by galaxy formation physics as well as the underlying
cosmology, fitting an observed correlation function yields valuable insights
into both. The calculation for a 2PCF involves computing pair-wise separations
and consequently, the computing time scales quadratically with the number of
galaxies. The next-generation galaxy surveys are slated to observe many
millions of galaxies, and computing the 2PCF for such surveys would be
prohibitively time-consuming. Additionally, modern modelling techniques require
the 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 for
the next generation surveys. Thus, calculating the 2PCF forms a substantial
bottleneck in improving our understanding of the fundamental physics of the
universe, and we need high-performance software to compute the correlation
function. In this paper, we present Corrfunc --- a suite of highly optimised,
OpenMP parallel clustering codes. The improved performance of Corrfunc arises
from both efficient algorithms as well as software design that suits the
underlying hardware of modern CPUs. Corrfunc can compute a wide range of 2-D
and 3-D correlation functions in either simulation (Cartesian) space or on-sky
coordinates. Corrfunc runs efficiently in both single- and multi-threaded modes
and 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 is
designed to be both user-friendly and fast and is publicly available at
https://github.com/manodeep/Corrfunc.
