CUTE solutions for two-point correlation functions from large cosmological datasets

TL;DR

This paper introduces CUTE, a free, parallelized code for efficiently computing two-point correlation functions in large cosmological datasets, leveraging multi-core CPUs and GPUs to handle massive data volumes.

Contribution

The paper presents a new open-source software tool optimized for large-scale cosmological data analysis using parallel computing techniques.

Findings

Efficient computation of two-point correlation functions on large datasets.

Parallel implementations using OpenMP and CUDA significantly reduce processing time.

Code availability facilitates broader adoption in cosmological research.

Abstract

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.