Writing efficient CUDA code is an art form. You can’t just "port" CPU code and expect it to be 100x faster. You have to manage:
From weather forecasting to molecular modeling, CUDA allows scientists to simulate complex systems in hours instead of weeks.
A typical CUDA program follows a specific workflow often called the : Host (CPU): Manages the main logic and prepares the data. Device (GPU): Executes the heavy-duty math.
Before writing custom kernels, check if cuBLAS (for linear algebra) or cuDNN (for deep learning) already has what you need. The Bottom Line
Moving data between the CPU and GPU is slow. If you spend more time moving data than processing it, your code will be sluggish.
Every major deep learning framework (TensorFlow, PyTorch) relies on CUDA kernels to perform the matrix multiplications required for neural networks.
You must ensure that threads access memory in a tidy, organized fashion to maximize bandwidth. 5. Getting Started