Two Parallel Sorting Algorithms for Massive Data

Abstract

As a basic operation, sorting has a wide range of applications. Early research provides a variety of serial sorting algorithms to speed up sorting. However, these serial algorithms waste the parallel computing capacity of current computers. On the other hand, it is of vital importance to promote the speed of sorting large-scale data because of the boom in data science, while traditional sorting algorithms are no longer suitable due to its relatively long complexity. Consequently, we propose two parallel sorting algorithms, Parallel Quick Sort Algorithm (PQSA) and Merging Subarrays from Quick Sort Algorithm (MSQSA) to solve this problem. In the meantime, we carry out simulation experiments to evaluate their performance. The experiment results show that two sorting algorithms run faster than traditional serial sorting algorithms. Furthermore, with the number of total threads increasing, PQSA and MSQSA have shorter running time and higher parallel speedup. Most importantly, when data size grows, MSQSA becomes slightly faster than PQSA, which shows its stronger practicability in sorting massive data.