Parallel Merge Sort with OpenMP

Welcome to our tutorial on implementing parallel processing in a merge sort algorithm!
In this video, we’ll explore how OpenMP, a powerful API supporting parallel processing in languages like C, C++, and Python, can make merge sort more efficient. We’ll start by understanding the intuition behind parallel processing through a detailed look at how merge sort works.
Here’s what we’ll cover:
Implementing parallel merge sort: Learn how to use OpenMP to efficiently sort large arrays of integers.
Sequential merge sort base case: Discover why and how it includes a sequential implementation for smaller subarrays.
Merge function: See how two sorted subarrays are merged into a single sorted subarray.
Reading input: Understand how the program reads input numbers from "numbers.txt".
Execution time: Learn to print the execution time for parallel mode.
OpenMP parallel sections: Discover how OpenMP divides the array into subarrays and sorts them concurrently.
Threshold for sequential sort: Understand the use of a threshold to switch to sequential sorting for smaller subarrays to avoid excessive overhead.
Efficiency: See how the parallel merge sort algorithm is more efficient than the sequential version for large datasets, especially on multi-core systems.
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