Ying focused on enhancing hardware compatibility and benchmarking reliability in the meta-pytorch/tritonbench repository by developing a Dynamic CUDA Availability Runtime Check. This feature, implemented in Python with CUDA and GPU programming expertise, detects current GPU capabilities and CUDA support at runtime, directly addressing the challenge of benchmarking across heterogeneous hardware. By aligning runtime detection with MTIA compatibility requirements, Ying reduced false incompatibility reports and established a more robust foundation for future GPU feature detection. The work emphasized reliability and extensibility, ensuring that benchmarking results are more trustworthy and applicable to a wider range of GPUs without introducing unnecessary complexity or overhead.