Considering the low positioning accuracy and strong scene dependence of optimization strategy in the Distance Vector Hop （DV-Hop） localization model， an improved DV-Hop model， Function correction Distance Vector Hop （FuncDV-Hop） based on function analysis and determining coefficients by simulation was presented. First， the average hop distance， distance estimation， and least square error in the DV-Hop model were analyzed. The following concepts were introduced： undetermined coefficient optimization， step function segmentation experiment， weight function approach using equivalent points， and modified maximum likelihood estimation. Then， in order to design control trials， the number of nodes， the proportion of beacon nodes， the communication radius， the number of beacon nodes， and the number of unknown nodes were all designed for multi-scenario comparison experiments by using the control variable technique. Finally， the experiment was split into two phases：determining coefficients by simulation and integrated optimization testing. Compared with the original DV-Hop model， the positioning accuracy of the final improved strategy is improved by 23.70%-75.76%， and the average optimization rate is 57.23%. The experimental results show that， the optimization rate of FuncDV-Hop model is up to 50.73%， compared with the DV-Hop model based on genetic algorithm and neurodynamic improvement， the positioning accuracy of FuncDV-Hop model is increased by 0.55%-18.77%. The proposed model does not introduce other parameters， does not increase the protocol overhead of Wireless Sensor Networks （WSN）， and effectively improves the positioning accuracy.

The domestic DCU （Deep Computer Unit） adopts the parallel execution model of Single Instruction Multiple Thread （SIMT）. When the programs are executed， inconsistent control flow is generated in the kernel function， which causes the threads in the warp be executed serially. And that is warp divergence. Aiming at the problem that the performance of the kernel function is severely restricted by warp divergence， a compilation optimization method to reduce the warp divergence time — Partial-Control-Flow-Merging （PCFM） was proposed. Firstly， divergence analysis was performed to find the fusible divergent regions that are isomorphic and contained a large number of same instructions and similar instructions. Then， the fusion profit of the fusible divergent regions was evaluated by counting the percentage of instruction cycles saved after merging. Finally， the alignment sequence was searched， the profitable fusible divergent regions were merged. Some test cases from Graphics Processing Unit （GPU） benchmark suite Rodinia and the classic sorting algorithm were selected to test PCFM on DCU. Experimental results show that PCFM can achieve an average speedup ratio of 1.146 for the test cases. And the speedup of PCFM is increased by 5.72% compared to that of the branch fusion + tail merging method. It can be seen that the proposed method has a better effect on reducing warp divergence.