What are some typical challenges faced by a CUDA GPU software engineer when optimizing code for parallel execution?

CUDA GPU Software Engineers often encounter challenges such as identifying sections of code that can be efficiently parallelized, managing memory bandwidth, and minimizing latency due to data transfers between CPU and GPU. Debugging and profiling GPU kernels can also be more complex than on traditional CPUs, requiring specialized tools. Additionally, engineers must ensure their code scales well across different GPU architectures, which can involve understanding hardware-specific constraints and optimizing for both speed and resource usage.