🧐 What Problem Does This Project Solve?

Computational efficiency is a critical concern in high-performance computing. This project investigates the performance trade-offs between parallel and sequential implementations for computationally intensive tasks, focusing on matrix multiplication.

🛠️ How Was the Problem Solved?

The project analyzed and implemented both parallel and sequential algorithms for matrix multiplication using OpenMP, employing data parallelism techniques to enhance computation speed. Key steps included:

• Developing OpenMP-based parallel programs, utilizing clauses and functions such as parallel for, collapse, private, num_threads, and timing functions like omp_get_wtime.
• Optimizing computational efficiency by exploiting multi-threading with a focus on thread management.
• Designing a flowchart to visualize and compare the algorithmic approach for both parallel and sequential implementations.

✅ How Close Did This Project Get to Solving the Problem?

The parallel implementation achieved a significant speed-up of 8.15 times compared to the sequential code. This demonstrates the effectiveness of leveraging OpenMP for parallel computation and highlights the potential for scaling performance in resource-intensive tasks. The findings contribute to optimizing performance in real-world high-performance computing applications.