Sorting Algorithm Performance Analysis

How can we analyze the performance of different sorting algorithms?

We conducted experiments on three sorting algorithms for arrays of varying sizes. How did the algorithms perform based on the recorded times?

Analysis of Sorting Algorithm Performance

Sorting algorithms are essential in computer science for organizing data efficiently. By comparing the recorded times for each array size using Bubble Sort, Quick Sort, and Merge Sort, we can evaluate the performance of these algorithms.

When we analyzed the recorded times for sorting arrays of 100, 1000, 10,000, 100,000, 500,000, and 1,000,000 random integers, we observed interesting trends in algorithm performance.

Bubble Sort

Bubble Sort, a simple sorting algorithm, showed increasing time complexity as the array size grew. For example, with an array size of 100, it took 1000 ns, while for an array size of 1,000,000, it took 50,000,000 ns. This indicates that Bubble Sort is less efficient for larger datasets.

Quick Sort

Quick Sort demonstrated better performance compared to Bubble Sort. The recorded times show that Quick Sort has a more consistent time complexity as the array size increases. For instance, with an array size of 100, Quick Sort took 500 ns, while for an array size of 1,000,000, it took 10,000,000 ns. This suggests that Quick Sort is a more efficient algorithm for sorting larger datasets.

Merge Sort

Merge Sort, known for its stability and guaranteed performance, exhibited the most efficient sorting capabilities among the three algorithms. The recorded times revealed a steady increase in time complexity with the growing array size. For example, with an array size of 100, Merge Sort took 200 ns, while for an array size of 1,000,000, it took 1,000,000 ns. This indicates that Merge Sort is highly suitable for large datasets due to its stable time complexity.

By analyzing the performance of Bubble Sort, Quick Sort, and Merge Sort on arrays of varying sizes, we can make informed decisions when choosing sorting algorithms for different use cases. Understanding the efficiency and stability of these algorithms is crucial for optimizing data processing tasks.