An Accuracy-Aware Implementation of Two-Point Three-Dimensional Correlation Function using Bin-Recycling Strategy on GPU

The analysis of scientific data, specially in different kinds of cosmological studies, has to deal with the increment in data volume. These studies include the calculation of correlation functions such as the Two-point Three-Dimensional Correlation Function. To get the final estimator value for these functions, it is necessary to construct histograms for storing large number counts. Histograms are a very common way of representing data and summarizing information in science. However, they have a high computational cost, which is worsened by the increase of the standard sample size. This increment leads directly to two problems: first of all, the large processing time and, secondly, the lack of accuracy of the result. Therefore, the implementations of correlation functions need to maintain high accuracy and affordable processing times. In order to reduce the high processing times, GPU computing is being widely used. In this work, the binrecycling strategy is implemented and evaluated in the Two-Point Three-Dimensional Correlation Function. We show that this implementation outperforms others which also correctly process a large number of galaxies. As a result of this work, an accuracy-aware implementation of the Two-Point Three-Dimensional Correlation Function on GPU is described and evaluated to ensure the correctness of the results.