# Acceleration of a Production-Level Unstructured Grid Finite Volume CFD Code on GPU
## 6 Conclusions  
In this study, different shared memory parallel methods were implemented for unstructured CFD applications on the GPU platform. Performance was further improved through three memory access optimization techniques. Performance tests were conducted using two three-dimensional unstructured-grid cases of the M6 wing and the CHN-T1 airplane. The specific main conclusions are presented as follows:  
1. Three parallel strategies based on loops have been implemented, among which the face colouring and reduction strategies are superior to atomic operations.
2. The reduction strategy combined with shared memory optimization has a significant acceleration effect. Compared to the serial CPU version, single GPU parallel computing can achieve an acceleration ratio of 127\(\times\).
3. Improving data locality has a significant effect on improving the computational performance of unstructured CFD. Using Cuthill-McKee grid renumbering and loop fusion techniques can improve the memory access performance by 10%.
4. The proposed multi-level memory-access optimization strategy can speed up the hot spot subroutine with data racing by three times.
5. The implicit algorithm part is the main obstacle to parallel scalability. Multiple colouring strategies have high concurrency, but the degrading of implicit property may affect convergence efficiency. The concurrency of hyperplane algorithms is not as high as  
Figure 14: Convergence history of M6 wing. (**a**) Residual versus iterations. (**b**) Residual versus time.  
Figure 15: Convergence history of CHN-T1. (**a**) Residual versus iterations. (**b**) Residual versus time.  
colouring, but it improves the convergence efficiency of LU-SGS because it reduces the bandwidth of the system matrix by cell reordering.
6. With the same number of MPI ranks, CPU+GPU heterogeneous computing can speed up more than 30 times compared with pure CPU computing to reduce the residuals to the same order of magnitude.  
There is still a great deal of potential for improvement in the performance optimization of production-level CFD software GPU parallel computing, such as communication/computing overlap, mixed precision computing, etc. Further research will be conducted in future work.  
Conceptualization, J.Z. and J.L.; methodology, L.D.; software, R.L. and Z.D; validation, J.Z., L.D. and J.L.; formal analysis, J.Z.; investigation, L.D.; resources, Z.D.; data curation, R.L.; writing--original draft preparation, J.Z.; writing--review and editing, L.D.; visualization, Z.D. and R.L.; supervision, J.L. and N.Z.; project administration, N.Z.; funding acquisition, J.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Numerical Wind Tunnel (NNW) Project of China, the Sichuan Science and Technology Program (2023 YFG0152), the National Key Research and Development Program of China (2021 YFB0300101).  
Not applicable.  
The data that support the findings of this study are available from the corresponding author upon reasonable request.  
The authors declare no conflict of interest.