Background: Sorting algorithms are an essential part of computer science. With the use of parallelism, these algorithms performance can improve.

Objectives: To assess parallel sorting algorithms performance compared with their sequential counterparts and see what contextual factors make a difference in performance.

Methods: An experiment was made with quicksort, merge sort, load-balanced parallel merge sort and hyperquicksort. These algorithms executed on Ubuntu 20.10 and Windows 10 Home with three data sets, small (10⁶ integers), medium (5  10⁶ integers) and large (10⁷ integers). Each algorithm executed 1 000 times per data set within each operating system resulting in 6 000 executions per sorting algorithm. 

Results: With the data from the executions, it was concluded that hyperquicksort had the fastest execution time. On average load-balanced parallel merge sort had the slowest execution time. The fastest operating system was Ubuntu 20.10, all but one algorithm executed faster on Ubuntu.

Conclusions: The results showed that the fastest algorithm was hyperquicksort, but other conclusions also arose. The data set size correlated with both the execution time and speedup for a given parallel sorting algorithm. When the data set size increased, both the execution time and the speedup increased.