<![CDATA[The rapid growth of data in the era of Big Data demands efficient and scalable algorithms to handle large datasets. Sorting, as a fundamental operation in data processing, plays a crucial role in various computational tasks. This study focuses on the performance analysis of the Parallel Merge Sort algorithm using the Message Passing Interface (MPI) to accelerate sorting operations on large-scale datasets. The implementation utilizes MPI for distributed memory communication across multiple processes, enabling concurrent data partitioning and merging. Experiments were conducted on datasets ranging from several hundred megabytes to multiple gigabytes to evaluate performance metrics such as execution time, speedup, and efficiency. The results demonstrate that the parallel implementation significantly reduces computation time compared to the sequential version, especially as the dataset size and the number of processes increase. However, the performance gain tends to decrease when communication overhead between MPI processes becomes dominant. Overall, the findings indicate that MPI-based Parallel Merge Sort is an effective approach for large-scale data sorting, providing a balance between computation and communication efficiency in parallel environments.]]>
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