## 5 Parallel Computing Architecture and Implementation
As this is the part of the model with the largest communication overhead, adopting the parallel mode of multi-core co-storage within the SMP processor node is the optimal strategy. OpenMP is an industry-standard framework for parallel programming in shared memory systems. It provides an application programming interface designed to write parallel programs on multiple processors or multi-core systems, with the advantages of simplicity, expandability, and ease of migration, eliminating the need for programmers to develop complex creation, synchronization, load balancing, and destruction operations. OpenMP includes a set of compilation instructions and a supporting library of functions, and it employs the Fork/join parallel execution model. At the shared storage level, the best method for parallelization is to adopt OpenMP. First, there is a clear advantage in relatively fine-grained parallel development with good expandability. Second, the concurrent computation of the loop is directed in the form of a directive statement in OpenMP, which is relatively easy to program and develop by adding a directive statement to the proper loop of the serial program for data processing. Third, there is no need for explicit communication in the OpenMP, avoiding a large amount of message-passing overhead during data processing, which well-suits the parallel characteristics of the DOL/parameters and the hardware characteristics of the SMP cluster node processor, thus effectively improving the computational efficiency. Each SMP cluster computing node process receives the allocated data information for each sortie. Based on the number of node CPU cores, the parameters are allocated to each core in turn for parallel operations, making full use of the CPU resources. Finally, the statistical processing results are stored locally within the node for preservation. Considering the fine-grained parallel algorithm based on OpenMP, it can be seen based on further analysis of the load spectrum processing model that fine-grained concurrency exists in each processing step. For example, the algorithm for peak-to-valley value detection in step (2) of Section 1 is used to sequentially extract the adjacent three points \(p_{1}\), \(p_{2}\), \(p_{3}\) in the for loop, from which the peak-to-valley value can be determined by calculating \((p_{2}-p_{1})(p_{2}-p_{3})>0\). Similar cyclic processes exist in other processes, such as effective amplitude detection and rain flow counting. Parallelization of the for loop is a very critical feature in OpenMP, and it can substantially enhance the execution efficiency when compared to traditional loop processing. The pseudo-code, after modification, is as follows:  
#Progma omp parallel for schedule (dynamic) { For (_i_ = firspoint, \(i\) < lastpoint; _i_++)  {  If (PeakValuedetec (point [_i_]))  /\"The PeakValuedetec () function calculates the positive and negative product of two differences at three adjacent points,  To determine if the midpoint is an extreme value*/  /\"Collectarray [_j_] stores the peak point for the first time*/  Collectarray [_j_] = point [_i_ + 1]  }  } }  
Through analysis of the above concurrency layers, two parallel solutions can be formed for the combination of different layers of concurrency, as shown in Figures 3 and 4. As can be seen from Figure 10 (Algorithm 1 is shown in Figure 10) Algorithm 1 takes the measured total take-off and landing data stream as the data input object. The computing node running the main thread is responsible for each take-off and landing as a unit, broadcasting and distributing the take-off and landing data storage address information. Each computing node in the cluster reads and stores the data information to be processed to local memory via parallel I/O, then within each computing node. The read take-off and landing information is distributed to each core processor for processing separately via parameter recycling based on the techniques of OpenMP.