Efficiency Comparison of Parallel and Serial Computation Techniques for Multi-Regional Weather Data Aggregation

In this study, the efficiency of parallel and serial computation techniques for aggregating data from diverse regions is investigated. Parallel computation breaks data into smaller segments and assigns each segment to a different processor or core, as opposed to traditional serial computation, which processes data sequentially. The research focuses on comparing the total amount of time needed by each approach to collect data from diverse regions in order to assess the effectiveness of parallel processing. According to the research, parallel computation significantly reduces the amount of time needed to collect data, and this correlation is directly related to the number of processors or cores used. The potential benefits of multi-core architectures in accelerating the gathering of data from multiple places are highlighted in this study. The outcomes highlight the efficacy of parallel computation methods, illuminating their potential to accelerate multi-regional data collection procedures in a variety of applications.