  In the advent of new large galaxy surveys, which will produce enormous
datasets with hundreds of millions of objects, new computational techniques are
necessary in order to extract from them any two-point statistic, the
computational time of which grows with the square of the number of objects to
be correlated. Fortunately technology now provides multiple means to massively
parallelize this problem. Here we present a free-source code specifically
designed for this kind of calculations. Two implementations are provided: one
for execution on shared-memory machines using OpenMP and one that runs on
graphical processing units (GPUs) using CUDA. The code is available at
http://members.ift.uam-csic.es/dmonge/CUTE.html.
