# Performance Evaluation Method for Intelligent Computing Components for Space Applications
## 4 Benchmark Execution and Evaluation
### High-Performance Computing Benchmark Experiment Results
#### 4.3.1 Test Results on GPUs  
The CUDA framework has advantages such as parallel computing capability, high-bandwidth memory, flexible kernel function programming, and optimized GPU acceleration libraries, making it capable of providing more efficient and faster computing capabilities for space artificial intelligence applications. The results in Table 3 are the average statistics after 20 experiments. The table displays the total execution time of 12 computational benchmark modules on the experimental platforms.  
\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}  & **GPU (Cuda)** & **GPU (Cuda\_Opt)** & **GPU (Cuda)** & **GPU (Cuda\_Opt)** & **GPU (Cuda)** & **GPU (Cuda\_Opt)** \\ \hline fast\_fourier\_transform & 4.705 & 0.400 & 2.984 & 0.294 & 30.962 & 3.601 \\ \hline finite\_impulse\_response\_filter & 1.043 & 0.523 & 0.214 & 0.788 & 4.582 & 2.454 \\ \hline LRN\_bench & 5.361 & 4.269 & 7.149 & 8.907 & 14.206 & 10.532 \\ \hline matrix\_multiplication\_bench & 138.326 & 47.547 & 336.830 & 127.513 & 51.823 & 56.630 \\ \hline convolution\_2D & 5.606 & 5.119 & 10.203 & 8.450 & 17.766 & 11.121 \\ \hline correlation\_2D & 8.141 & 5.234 & 18.737 & 19.482 & 93.056 & 14.723 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Benchmark experiment results of CUDA framework (unit: ms).  
\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}  & **GPU (Cuda)** & **GPU (Cuda\_Opt)** & **GPU (Cuda)** & **GPU (Cuda\_Opt)** & **GPU (Cuda)** & **GPU (Cuda\_Opt)** \\ \hline max\_pooling & 2.509 & 2.152 & 4.717 & 4.200 & 8.537 & 8.724 \\ \hline cifar\_10 & 0.457 & 0.431 & 0.772 & 0.651 & 16.956 & 16.876 \\ \hline cifar\_10\_muipple & 73.995 & 72.100 & 283.187 & 197.027 & 790.402 & 787.754 \\ \hline relu\_bench & 4.704 & 4.302 & 7.280 & 6.424 & 14.973 & 12.910 \\ \hline softmax\_bench & 6.827 & 4.305 & 10.679 & 7.025 & 44.284 & 12.680 \\ \hline wavelet\_transform & 0.221 & 0.287 & 0.293 & 0.431 & 5.713 & 1.672 \\ \hline \hline \end{tabular}
\end{table}
Table 3: _Cont._The GPUs used in the three experimental platforms are NVIDIA Volta GV10B, NVIDIA 940 MX, and Veiglo BI-V100. From the table, it can be seen that for some simple computational modules, the execution time difference among the three platforms is relatively small. However, for complex computational modules like the CIFAR-10 inference chain, the computational power of Jetson AGX Xavier is fully utilized, and the execution time is 4 times faster than that of the ASUS FL8000 laptop and 10 times faster than that of the Loongson platform.  
Figure 5 shows that Jetson AGX Xavier always has the fastest execution time, demonstrating its powerful computing performance. The Loongson experimental platform performed poorly in the inference of cifar_10. However, after optimized processing, the execution time of convolution_2D and correlation_2D operations was significantly shortened, and the difference with Jetson AGX Xavier was significantly reduced. This indicates that the Loongson platform also has certain advantages in some specific operations.