# Performance Evaluation Method for Intelligent Computing Components for Space Applications
## 4 Benchmark Execution and Evaluation
### Typical Application Benchmark Experimental Results
#### 4.4.1 Image Calibration Benchmark Test Results  
The throughput and power results of the image calibration experiment are shown in Tables 5 and 6. To improve the accuracy of the experiments and avoid exceptional cases, an average of 20 experimental results was taken for each platform under the same experimental conditions, measured in Mpixels/s. Five standard image sizes were used in the experiment: 1024 \(\times\) 1024, 2048 \(\times\) 2048, 4096 \(\times\) 4096, 8192 \(\times\) 8192, and 10240 \(\times\) 10240. Fixed seed-generated quasi-random data were used as input data. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Benchmark Name**} & \multicolumn{2}{c}{**Jetson AGX Xavier**} & \multicolumn{2}{c}{**ASUS FL8000**} & \multicolumn{2}{c}{**Loongson Platform**} \\ \cline{2-7}  & **CPU (Omp)** & **CPU (Omp\_Opt)** & **CPU (Omp)** & **CPU (Omp\_opt)** & **CPU (Omp)** & **CPU (Omp\_Opt)** \\ \hline fast\_fourier\_transform & & & & & & \\ transform & 0.247 & 0.813 & 0.056 & 0.357 & 0.210 & 1.847 \\ \hline finite\_impulse\_response\_filter & 1.295 & 0.675 & 0.860 & 0.595 & 2.936 & 1.714 \\ \hline LRN\_bench & 22.352 & 17.231 & 16.544 & 12.149 & 86.929 & 77.217 \\ \hline matrix\_multiplication\_bench & 1824.736 & 212.291 & 1592.956 & 236.281 & 2020.820 & 928.384 \\ \hline convolution\_2D & 15.341 & 9.861 & 27.403 & 15.344 & 160.522 & 114.807 \\ \hline correlation\_2D & 5.103 & 4.332 & 8.894 & 6.218 & 85.302 & 58.161 \\ \hline max\_pooling & 1.671 & 1.542 & 3.910 & 1.873 & 18.803 & 9.848 \\ \hline cifar\_10 & 1.330 & 1.013 & 0.996 & 0.617 & 7.686 & 2.702 \\ \hline cifar\_10\_muipule & 488.251 & 299.511 & 92.495 & 79.795 & 481.898 & 468.590 \\ \hline relu\_bench & 3.076 & 2.824 & 4.104 & 2.895 & 14.400 & 8.733 \\ \hline softmax\_bench & 5.662 & 4.890 & 12.829 & 7.033 & 54.569 & 53.433 \\ \hline wavelet\_transform & 1.255 & 0.689 & 0.891 & 0.353 & 6.032 & 1.680 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Benchmark experiment results of OpenMP framework (unit: ms). In Table 5, the throughput of the CUDA framework is greater than that of the OpenMP framework for each experimental platform when inputting an image of the same size, and the difference in the execution results is more significant for the Loongson platform. Under the OpenMP framework, the throughput of the Jetson AGX Xavier platform is always the largest, but under the CUDA framework, the throughput of the Loongson platform surpasses that of Jetson AGX Xavier when the input image size increases to 4096 \(\times\) 4096, though the power consumption of the Loongson platform is higher as well. In Table 6, when the input image size is 10,240 \(\times\) 10,240, the power consumption of the Loongson platform is 4.67 times that of Jetson AGX Xavier (in the CUDA framework). Figure 7 presents a comparison that is more user-friendly and easier to understand. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Image Size**} & \multicolumn{4}{c}{**Throughput**} \\ \cline{2-7}  & **Jetson AGX Xavier** & \multicolumn{2}{c}{**ASUS FL8000**} & \multicolumn{2}{c}{**Loongson Platform**} \\ \cline{2-7}  & **CPU (Omp)** & **GPU (Cuda)** & **CPU (Omp)** & **GPU (Cuda)** & **CPU (Omp)** & **GPU (Cuda)** \\ \hline
1024 \(\times\) 1024 & 3.27 & 25.78 & 2.46 & 3.88 & 0.44 & 22.05 \\
2048 \(\times\) 2048 & 3.41 & 29.20 & 1.12 & 3.96 & 0.30 & 28.97 \\
4096 \(\times\) 4096 & 2.57 & 31.15 & 1.10 & 4.01 & 0.24 & 32.36 \\
8192 \(\times\) 8192 & 2.09 & 30.93 & 0.94 & 4.02 & 0.24 & 37.02 \\
10,240 \(\times\) 10,240 & 2.28 & 29.91 & 1.00 & 4.01 & 0.19 & 37.34 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Throughput of each experimental platform under Image Calibration Benchmark (unit: Mpixels/s).