# Co-Processing Parallel Computation for Distributed Optical Fiber Vibration Sensing
## 4 Experimental Results and Discussions
### Extended Comparison Experiments and Discussions  
In addition, we also did the experiment in long distance conditions to verify the feasibility and effectiveness of our parallel computation method based on the GPU. The lengths of optical fiber are 2020, 4430, 9832 and 14,130 m, respectively. The differential accumulation algorithm and the FFT processing both are executed on the CPU and GPU. The experiment results are shown in Figure 15.  
Figure 14: The time contrast of FFT processing on CPU and GPU: (**a**) the time spent on CPU (**b**) the time spent on GPU. Figure 13: The obtained frequency–space map after processed with GPU: (**a**) for single-point vibration (**b**) for two-points vibration. The time contrast between the CPU and GPU for the differential accumulation algorithm is shown in Figure 15a when the number of RBS curves is 2000. The red bar chart shows the time consumed on the CPU, and the longer the distance, the longer the time consumed. The blue bar chart shows the time consumed on the GPU. The changing trend of time is not regular, but floats up and down with the microsecond level. This phenomenon demonstrates that the time consumption of the differential accumulation algorithm on the GPU may be limited by many factors, which is not only related to the optical fiber distance, but also may be influenced by the thread cache allocation or the thread switching process in the GPU, which also has an uncertain time consumption less or close to one microsecond. In the condition of the same amount of data, the time spent on the GPU is far less than that of the CPU. It can be demonstrated that the method of combining the GPU and CPU can realize fast data processing in relation to the differential accumulation algorithm for a \(\Phi\)-OTDR vibration sensing system. The time contrast between the CPU and GPU for FFT processing is shown in Figure 15b when the point number of FFT is 8192. The red and blue bar chart represents the time consumption of FFT processing on the CPU and GPU, respectively. With the increase in distance, both CPU and GPU show the same trend of growth. Moreover, in the condition of same amount of data, the time spent on the CPU is 100 times more than that of the GPU. It demonstrates that our parallel computation based on the GPU can shorten substantially the time of FFT analysis processing and optimize the real-time performance of the \(\Phi\)-OTDR sensing system. Evidently, the feasibility and validity of our parallel computation method based on the GPU are verified under long distance and large data volume conditions for the differential accumulation algorithm and FFT processing. Therefore, this paper verifies that the distributed optical fiber sensor is another example of a GPU application. Because of the large amount of sensing data and real-time performance requirement in practical applications of distributed optical fiber sensors, the GPU is used as an effective solution to improve the speed of data processing with a strong parallel computing capability. Based on the above discussion, the differential accumulation algorithm and FFT processing on the GPU could lead to less time being consumed. However, the use of the GPU will also bring the problem of increasing cost. Therefore, the identification of when the GPU will be necessary for real-time operation could be our next research direction. In this study, we only put forward a preliminary mathematical criterion which could be optimized for real application in the GPU. In the experimental setup, the maximum real-time data throughput is 100 MB/s and the data acquisition speed is 50 MSa/s. If each sampling point is set to be represented by 8 bits (1 Byte), the acquisition time (\(T_{\rm a}\)) and the transmission time (\(T_{\rm t}\)) could be simplified as \(T_{\rm a}=2\) s and \(T_{\rm t}=1\) s for the same data volume of 100 MB.