## 4 Experiment and Analysis
### Experimental Results  
In order to verify the effectiveness of GPU mapping implementation based on the SAR-SIFT algorithm, multi-GPU collaborative acceleration strategy for large area SAR image matching, and other algorithms and acceleration strategies, this experiment adopted 21 high resolution GF-3 SAR images in Ultra Fine Strip mode. Five experiments were carried out:  
1. Implementation of SAR-SIFT algorithm for single CPU server node This experiment uses a single CPU server node (without a GPU card), and the configuration of the subsequent experimental server nodes is consistent except for the absence of a GPU card. The implementation experiment of the CPU-based SAR-SIFT algorithm is carried out, and the time for extracting and matching 21 scenes GF-3 SAR images is counted as the benchmark algorithm, which is used to achieve GPU mapping with the proposed SAR-SIFT algorithm, the multi-GPU collaborative acceleration strategy and acceleration strategy for SAR image matching in large regions.
2. Implementation of SAR-SIFT algorithm for single GPU server node This experiment uses one GPU server node (including one GPU card) to carry out an experiment on the implementation of the SAR-SIFT algorithm based on a single GPU card. The time required for feature point extraction and matching 21 scenes GF-3 SAR images is counted to verify the acceleration efficiency of GPU mapping based on the SAR-SIFT algorithm in Section 3.1.
3. Implementation of SAR-SIFT algorithm for multiple image standardization acceleration of single GPU server node This experiment uses one GPU server node (including one GPU card) to carry out a SAR-SIFT algorithm implementation experiment based on multiple image standardization acceleration using a single GPU card. The time required for feature point extraction and matching 21 scenes GF-3 SAR images is counted to verify the standardization processing of the image data to be matched based on the GPU memory upper limit in the multi-GPU collaborative acceleration strategy for SAR image matching towards large areas in Section 3.2.
4. Implementation of SAR-SIFT algorithm for multiple image standardization acceleration of four GPU server nodes This experiment uses 4 server nodes (8 GPU cards in total) to carry out the SAR-SIFT algorithm implementation experiment based on multiple image standardization acceleration using 8 GPU cards. The experiment calculates the time required for feature point extraction and matching 21 scenes GF-3 SAR images to verify the effectiveness of the multi-GPU nodes collaborative acceleration strategy based on multi-GPU nodes collaborative parallel in Section 3.2 of SAR image matching for large areas.
5. Implementation of SAR-SIFT algorithm for multiple image standardization acceleration of 16 GPU server nodes This experiment uses 16 server nodes (32 GPU cards in total) to carry out the SAR-SIFT algorithm implementation experiment based on multiple image standardization acceleration using 32 GPU cards. The experiment calculates the time required for feature point extraction and matching 21 scenes GF-3 SAR images, to verify the effectiveness of the multi-GPU nodes collaborative acceleration strategy based on multi-GPU nodes collaborative parallel in Section 3.2 of SAR image matching for large areas.