Tools to measure clustering are essential for analysis of Astronomical datasets and can potentially be used in other fields for data mining. The Two-point Correlation Function (TPCF), in particular, is used to characterize the distribution of matter and objects such as galaxies in the Universe. However, it's computational time will be restrictively slow given the significant increase in the size of datasets expected from surveys in the future. Thus, new computational techniques are necessary in order to measure clustering efficiently. The objective of this research was to investigate methods to accelerate the computation of the TPCF and to use the TPCF to probe an interesting scientific question dealing with the masses of galaxy clusters measured using data from the Planck satellite. An investigation was conducted to explore different techniques and architectures that can be used to accelerate the computation of the TPCF. The code CUTE, was selected in particular to test shared-memory systems using OpenMP and GPU acceleration using CUDA. Modification were then made to the code, to improve the nearest neighbour boxing technique. The results show that the modified code offers a significant improved performance. Additionally, a particularly effective implementation was used to measure the clustering of galaxy clusters detected by the Planck satellite: our results indicated that the clusters were more massive than had been inferred in previous work, providing an explanation for apparent inconsistencies in the Planck data
