Abstract: With the increasing demand for computing power, various domestically produced heterogeneous computing devices have emerged. These devices have their specialized programming models, and developers need to develop based on the architecture characteristics of different devices using these dedicated programming models. Therefore, the code developed is not portable across devices. In recent years, unified heterogeneous parallel programming models that support various computing devices have appeared overseas, but there is still relatively little research and implementation of heterogeneous programming models for domestically produced devices. To address this issue, a performance-portable heterogeneous programming model called ParM has been developed. This programming model is provided in the form of a C++  library and shields many low-level implementation details, reducing the difficulty of parallel programming. The current backend devices supported by this programming framework include x86 CPUs, NVIDIA GPUs, Huawei Kunpeng processors, and Huawei Ascend AI processors. Performance optimizations have been carried out for these backend devices, and performance test on various devices has shown that the ParM programming model can achieve over 90% performance compared to native code.

Key words: performance portability, parallel programming model, high performance computing, heterogeneous computing, domestic processor