A big comparison of the basic sorting algorithms

Sorting is one of the main, prominent functionalities of most computer applications and games. We encounter it every time a computer sorts contacts alphabetically, or when a game renders a 2d scene (first, it sorts the objects according to the distance from the camera and then it draws them starting from the back). Sorting is also used when we simply need an amount of data according to some criterion for statistics. For example, sorting students according to their grades at school, employees according to their salary, candidates according to the number of votes they received, etc. Sorting (along with searching) is one of the main requirements of large, commercial information systems. It's speed is the most important property and it depends (disregarding the computer's speed) on the time complexity of the chosen sorting algorithm as well as on the structure of the processed data. If we know principles and time complexities of sorting algorithms, we can save huge amounts of time in our applications. Even operations that take hours to complete can be done in a few seconds when using the right algorithm.

The following work will compare the performances (benchmark) of six different sorting algorithms. The goal is to compare the relative speed of these algorithms on the same machine and on the same testing data, which includes lots of varied items. This should confirm the expected behavior and rate the algorithms according to the time they spend on sorting the items. This way, we'll find out which algorithms should be used for certain types of data and which should not.

Describing the testing conditions

During this benchmark, I tested 6 sorting algorithms based on the mutual comparison of items. Shown below, we have a list of the algorithms, their functionality description, implementation, and time complexity:


 

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A big comparison of the basic sorting algorithms, i.e. selection sort, bubble sort, merge sort, heap sort, and quick sort.

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Article has been written for you by David Jancik
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