Abstract: With the development of intelligent computing and big data applications, the demand for accelerators such as GPU is increasing. Computing software stacks such as CUDA and OpenCL software stacks are the key to making full use of GPU hardware performance. Considering the portability and implementation of software stacks on domestic fundamental OS and hardware platforms (such as Phytium CPU and Kylin OS) in future, this paper focuses on open-source OpenCL software stacks. The performance of OpenCL applications on different platforms is tested and analyzed. The performance of GPU computing on different OpenCL software stacks, such as Mesa, ROCm, etc., is evaluated. The impact of drivers and kernels in the software stack on GPU computing is evaluated. The entire test covers the time overhead of various stages of OpenCL calculations such as compilation, data transmission, and kernel execution. The test and evaluation found that it is more urgent and more suitable to use GPU for accelerated computing on domestic platforms. ROCm is an ideal OpenCL open source software stack with better performance and stability, and can be further optimized compared with close-source software stacks. 

Key words: OpenCL, computing software stack, GPU computing, domestic fundamental software and hardware platform