# Co-Processing Parallel Computation for Distributed Optical Fiber Vibration Sensing
## 5 Conclusions  
In this paper, the feasibility of speeding up the data processing rate of the \(\Phi\)-OTDR sensing system by using the method of cooperation between the CPU and GPU processing is discussed and experimentally verified. Two typical signal processing methods for the \(\Phi\)-OTDR sensing system are realized in parallel operations based on the GPU and traditional serial computations based on the CPU, respectively. For the differential accumulation algorithm which can achieve vibration detection and location, serial computations only based on the CPU take about 21,250 times as much execution time as parallel operations in the condition of same data volume. Thus, the GPU greatly accelerates the processing speed of the differential accumulation algorithm for the \(\Phi\)-OTDR sensing system. For the FFT analysis which can precisely achieve vibration location and spectrum analysis, the computation time with the GPU is approximately 140 times faster than serial computation under same experimental condition. It shows that the GPU can effectively improve the FFT processing speed of the \(\Phi\)-OTDR sensing system. In addition, the fast data collection and transmission are achieved by using data acquisition system based on FPGA. In conclusion, this novel data co-processing parallel computation method, which combines FPGA technology with the CPU and GPU accelerator, can realize data acquisition, transmission, and processing in parallel, and eventually optimize the real-time performance of the \(\Phi\)-OTDR sensing system.  
In this paper, we use the underlying CUDA API to realize multi-thread parallel computing in the GPU for distributed optical fiber sensors; this function encapsulation may become a research direction for embedded and portable application requirements, with the challenges of function optimization and algorithm simplification. In addition, in order to improve the processing speed of the whole system, collaborative parallel research into multi-processors will be a potential research direction. For example, further research on the parallel operations of several CPUs or several GPUs may be conducted, which could promote the parallel operation of a data processing algorithm in a \(\Phi\)-OTDR system. Therefore, multi-processors may be a parallel operation solution for the academic and industry communities.  
Conceptualization, Y.W.; Data curation, Y.L.; Investigation, Q.B. and Y.X.; Methodology, Y.W.; Project administration, Y.X.; Resources, B.J. and Y.X.; Software, Y.L.; Visualization, Y.C.; Writing--original draft, Y.L.; Writing--review & editing, Y.W., X.L., Y.C. and B.J. All authors have read and agree to the published version of the manuscript.  
This work was supported in part by the Foundation of Science and Technology on Near-Surface Detection Laboratory under Grant 6142414180206. This work was supported in part by the National Natural Science Foundation of China under Grant 61501468. This work was supported in part by the Key Research and Development (R&D) Projects of Shanxi Province under Grant No. 201803D121071. This work was supported in part by the Coal-Bed Methane Joint Research Fund of Shanxi Province under Grant No. 2015012005 and Grant No. 2016012011. This work was supported in part by the Natural Science Foundation of Shanxi Province under Grant 201701D221115. This work was supported in part by the Research Project Supported by Shanxi Scholarship Council of China under Grant 2016-035.  
The authors declare no conflict of interest.