How to Keep CUDA GPU Code Separate from Standard C++ in Mixed Projects

Developers working with CUDA can organize their codebases by placing GPU kernels and device memory logic in .cu files while keeping standard application logic in .cpp files. This separation prevents CUDA-specific syntax from interfering with regular C++ compilation, since standard compilers do not recognize keywords like __global__ and __constant__. To bridge the two, CUDA symbols are forward-declared in C++ files without CUDA qualifiers, allowing the linker to resolve them at build time. Kernel launches from .cpp files require the cudaLaunchKernel API instead of the usual chevron syntax, which is only supported by the NVIDIA compiler. This approach improves code readability and maintainability in projects that mix GPU-accelerated and CPU-only components.