What are some common challenges CUDA software engineers face when optimizing code for GPU performance?

Cuda Software Engineers often encounter challenges related to memory management, such as minimizing data transfers between CPU and GPU and optimizing memory access patterns to avoid bottlenecks. Additionally, ensuring code scalability across different GPU architectures and achieving efficient parallelization can be complex. Collaborating closely with data scientists, hardware engineers, and other developers is essential to troubleshoot performance issues and maximize throughput in real-world applications.