## 5 Parallel Computing Architecture and Implementation  
It is of great significance to determine and extend the service life of aircraft structures through the use of flight test data. The large volume of data collected in the process of actual measurement require statistical processing, following which the load time history can be compiled. In recent years, the amount of measured data has expanded significantly, encompassing full aircraft load measurements, the total number of takeoffs and landings for each measured flight profile, and nearly one thousand recorded parameters, such that the volume of the collected data points is on the order of tens of billions. This dramatic increase in data volume has led to longer processing times, affecting the efficiency of load spectrum compilation [14; 15; 16; 17]. Considering the difficulty of adapting to the real-time requirements of certain application and analysis scenarios, it has become imperative to fully harness the computational capabilities of existing devices to address the rapidly increasing demands for data processing. Parallel computing approaches have recently experienced great progress and have been widely used in weather forecasting, vehicle simulation, finite element analysis, petroleum exploration, and other fields that require high-performance scientific computation of large and complex signals. A small-scale symmetric multi-processing (SMP) cluster was used as the computing platform in this study. Based on analysis of the actual data processing model, two parallel computing techniques were used, and two parallel algorithms were developed for actual load data processing. The experiments indicate that the algorithm and system are reasonable, reducing the operation time of actual data processing and greatly improving the processing efficiency. According to the Flynn classification method [18; 19; 20; 21; 22; 23], the load spectra data processing system model belong to the Single Instruction Multiple Data Stream. In practical work, the system adopts a two-layer architecture based on SMP clusters, as shown in Figure 9.  
A two-tier architecture was adopted, including an SMP cluster computing node set and a measured data server. These components are interconnected with gigabit high-speed  
Figure 9: Parallel measurement data processing model based on SMP cluster. LANs. The set of computing nodes used in the experiment was a multi-core SMP cluster of multi-core PC computers within a local area network, running the process as follows:  
1. The cluster's main process is a computation strategy running in a parallel node configuration. For the initial computing parameters, a connection is established with the load spectrum measured data server. 2. The master process node scans the actual data according to the specified path in the database server, statistically generates a data storage address catalog (in terms of each flight), and distributes the storage address information to other computing nodes according to the set parallel strategy. 3. Each computing node reads the actual data of the sorties to be processed from the server based on the received address information and stores it locally. Then, the full flow of data statistical processing begins, and the resulting information of each landing is written into the local memory after completion of the calculation, which is sent to the main process computing node as a message. 4. The main process computing nodes are statistically summarized in terms of sections, takeoff, and landing counts and displayed using a graphical interface. 5. The main process computing node completes the task of cataloging. The computing node in the cluster is the main facilitator of the data processing flow for load spectrum compilation, which is the most time-consuming and complex process of calculation in the data processing flow. For each measured parameter, all statistical processing steps need to be completed, and there is a strong order relationship between the steps.