Performance and precision of histogram calculation on GPUs: Cosmological analysis as a case study

Histogram calculation is an essential part of many scientific analyses. In Cosmology, histograms are employed intensively in the computation of correlation functions of galaxies, as part of Large Scale Structure studies. Among the most commonly used ones are the two-point, three-point and the shear–shear correlation functions. In these computations, the precision of the calculation of the counts in each bin is a key element for achieving the highest accuracy. In order to accelerate the analysis of increasingly larger datasets, GPU computing is becoming widely employed in this field. However, the recommended histogram calculation procedure becomes less precise when bins become highly populated in these sort of algorithms. In this work, an alternative implementation to correct this problem is proposed and tested. This approach is based on distributing the creation of histograms between the CPU and GPU. The implementation is tested using three cosmological analyses with observational data. The results show an increased performance in terms of accuracy while keeping the same execution time.