# Architecture Exploration of a Backprojection Algorithm for Real-Time Video SAR
## 4 Experimental Results
### Experimental Environments  
The proposed architectures were implemented on a system equipped with Intel Xeon CPU with 128-GB main memory and four Nvidia RTX 3090 GPUs. The GPU has 82 streaming multiprocessors and 128 CUDA cores per SM. It has six copy engines and 24-GB global memory. By taking the I/O, range compression, and image formation modules from the open-source [33], we implemented the baseline architectures of Arch. F1 and S1 in C/C++. The overall performances of the baseline architectures were used as references for performance comparison. We implemented the full-aperture and the sub-aperture processing architectures in C/C++ for multi-thread parallelization in GPU devices. Starting from the baseline architectures, we migrated the range compression and the image formation from the C/C++ codes to CUDA C codes in Microsoft Visual Studio 2019 and CUDA Toolkit 11.3. The RC stage consisted of the H2D data transfer and the RC kernel. The IF stage consisted of the IF kernel and the D2H data transfer. All variables for RC and IF kernels are of float data type.  
To evaluate the performance of the BP-VISAR architectures, we used the AFRL Gotcha circular SAR dataset, which consisted of eight complete circular passes, with each pass at a different elevation angle. The dataset consisted of SAR phase history data collected at X-band with a 640 MHz bandwidth [31]. The phase history data collected for one degree of azimuth was stored in a file in the dataset.  
We set \(N_{\mathrm{s}}\) to 117, the number of pulses corresponding to one degree of azimuth in the dataset. We set \(N_{p}\) to 585, which was the five multiples of \(N_{\mathrm{s}}\). This configuration of synthetic aperture length resulted in the cross-range resolution of 0.26 m for video SAR frames. Different numbers of \(N_{\mathrm{s}}\) and \(N_{p}\) were configured for different cross-range resolutions or computational loads considering the requirements for target BP-VISAR applications.  
The average elapsed times for generating one BP-VISAR frame were measured for the architectures explored in non-overlapped and overlapped modes. For efficient target tracking, it is necessary to increase the overlap ratio more or raise the center frequency. The choice of the overlap ratio does not affect the imaging quality. However, if the overlap ratio is too close to 100%, the newly generated video SAR frame may not have sufficient  
Figure 7: Multi-GPU processing of Arch. S3: stream processing.  
changed information for target tracking but waste the computational resources. The overlap ratio of 80% was used for the overlapped mode. The target frame rate needed to be set according to the requirements of the BP-VISAR system applications under certain limitations, such as the cross-range resolution and the computational resources. Therefore, we evaluated the performance of the BP-VISAR architectures in the best-effort mode in which the architectures run as fast as possible. This evaluation method measured the performance improvement by the architectures that were explored relative to the reference BP-VISAR architectures in a given computing platform.  
To investigate the changes in the visual quality of video SAR frames during the acceleration process, we used the PSNR to compare the quality of video SAR frames generated in each architecture with those in the baseline architectures.