## 5 Parallel Computing Architecture and Implementation
Each core is independent of the others in parameter units and runs in parallel, effectively improving the operational processing efficiency. The results of statistical processing of each thread are eventually sent as messages to the main thread node for sub-section and full-section merging, the results of which are then used in the subsequent load spectrum compilation process. As can be seen in Figure 11 (Algorithm 2 is shown in Figure 12) uses a combination of medium- and fine-grained parallelization, unlike Algorithm 1, which uses a single takeoff and landing data stream as the initial processing object. The main thread computing node uses a serial mode, reading the data information for each takeoff and landing in turn. Each computing node is no longer assigned unit landing information as in Algorithm 1. Instead, more specific unit parameter data information is assigned. The statistical processing of parameters also differs from Algorithm 1 but is also parallelizable program code based on the OpenMP technology. Although Algorithm 2 introduces increased program complexity compared to Algorithm 1, it offers significant advantages in enhancing the speed of parallel operations and fully leveraging the computational potential of multi-core processors. For the processing of the measured load spectrum data of a certain type of aircraft, a small SMP cluster was built, including four dual-core PC nodes with an Intel Core 2 dual-core E7500 processor, 2930 MHz CPU main frequency, 128 kB cache, and 3 GB of memory. The nodes were connected via a 1000 M local area network, Windows 7 was used as the software operating system, and the MPIH2.0 version of the parallel computing environment was used for the MPI. During statistical processing of the measured load spectrum data associated with the 106-type aircraft's takeoffs and landings, comprising 302 parameters per takeoff and landing, the system's operational performance was determined, as shown in Figures 12 and 13. Figure 11: Medium- and fine-grained parallel data processes based on each deformation data stream. Figure 10: Coarse- and medium-grained data processing flows based on aircraft type data flow. As can be seen from Figure 12, as the number of computing nodes participating in the operation increased, the overall running time required for both parallel approaches constantly decreased, with the overall trend decreasing and gradually flattening out. Comparatively, mode 1 was more efficient than mode 2 in improving processing efficiency. According to our analysis, I/O operations between processes and LAN communication latency were the main reasons for this trend; in particular, mode 1 reads all the data per takeoff and landing at one time in a multi-threaded parallel I/O mode, while mode 2 reads the data per takeoff and landing in turn, improving the I/O efficiency considerably. Mode 2 has the advantage of fine-grained parallel optimization in the statistical processing step, which requires less time to complete the operation for a single parameter. Overall, however, I/O operations will have a greater impact on the overall system uptime. In addition, as illustrated by the acceleration ratio depicted in Figure 13 relative to the number of CPUs, it can be seen that the maximum acceleration ratio of Mode 1 reached 5.82 under laboratory conditions, with four computing nodes and eight CPUs participating in the operation, providing a satisfactory improvement in execution efficiency. Based on the obtained results, it is also possible to conduct extended research in clustered systems with a higher number of computing nodes. Figure 12: Relationship between running time and number of computing nodes. Figure 13: Relationship between acceleration ratio and number of CPUs.