Probability Algorithms for Data Statistics and Optimization Analysis of Parallel Systems

In the context of big data era, data processing and analysis are facing unprecedented challenges, especially the real-time processing and efficient analysis of massive data have become urgent problems. This study focuses on the parallelization and system optimization of probabilistic algorithms, aiming to improve the efficiency and accuracy of data processing by technical means. In terms of specific methods, we first deeply analyze where the bottlenecks of existing probabilistic algorithms lie when dealing with big data, and then design and implement an efficient parallelization framework. The framework makes full use of the computational power of multi-core processors, and realizes the parallelization of probabilistic algorithms through the strategies of task decomposition, data allocation and parallel execution. Meanwhile, we also optimize the system architecture, including resource scheduling, load balancing, and communication optimization, to ensure the stable operation and efficient execution of the parallelization algorithm. In our experiments, we selected several typical datasets for testing and compared the performance before and after parallelization. As the number of threads increases (from serial to 2-thread, 4-thread to 8-thread), the processing time is gradually shortened, which shows that parallel processing is very effective in accelerating computation. The method in this paper provides an efficient and reliable solution for big data processing and analysis, which helps to promote digital transformation and intelligent upgrading in various industries.