Modeling and Application of Performance Optimization for Parallel Algorithms

In many large-scale scientific and engineering research fields, simulation computing has played a significant role in its development. This type of computing has the characteristics of high complexity and large computational load, and traditional serial algorithms can no longer meet their computing needs. Parallel processing has become a key technology for solving problems. How to give full play to the performance of highperformance computing systems and design parallel algorithms with optimal performance has become a hot issue in current research. This paper proposes a parallel algorithm performance optimization analysis model to guide the design and optimization of parallel algorithms. This model quantitatively analyzes the design optimization of parallel algorithms from two aspects: parallel systems and parallel algorithms. It provides expressions for the acceleration, efficiency, cost, and other indicators of the algorithm, and points out performance bottlenecks in the algorithm. This provides theoretical guidance for maximizing the performance of parallel systems and optimizing parallel algorithms. Based on this model, this article conducts research and analysis on the parallel optimization design of large matrix multiplication algorithms, and conducts experimental verification, greatly improving the parallel efficiency of matrix multiplication.