CUDA developers can now isolate GPU kernels in dedicated .cu files while keeping the rest of the program in standard .cpp files. This organization confines CUDA-specific code to a limited set of files, making the overall project easier to maintain and understand.
The approach uses two files: kernel.cu, which defines the GPU kernel, and main.cpp, which launches the kernel and handles error checking. In kernel.cu, only the kernel function marked with `__global__` is defined. For example, a minimal `noopKernel` that performs no work can serve as a starting point for verifying the separation.
Launching the Kernel from C++
In main.cpp, the kernel must be declared with `void noopKernel();` before it can be launched. The launch uses `cudaLaunchKernel`, passing the kernel's address, grid and block dimensions (both set to `dim3(1,1,1)` for a single thread), a null pointer for kernel arguments (since the kernel takes none), zero shared memory, and the default stream.
Error checking is performed with a helper function that prints the operation name and error message if a CUDA API call fails. After launching, two checks are made: `cudaGetLastError` catches launch configuration errors, and `cudaDeviceSynchronize` waits for GPU execution and reports runtime errors.
Compilation and Linking
Compilation requires compiling each source file separately: `nvcc -c kernel.cu` produces kernel.o, and `g++ -c main.cpp` produces main.o. These object files are then linked with `nvcc kernel.o main.o -o app`. Running the resulting executable prints "noopKernel completed successfully." on success.
Benefits of Separation
Separating kernels into .cu files keeps CUDA-specific code in one place, allows ordinary C++ code to be compiled with standard compilers like g++, and clearly separates GPU computation from application logic. This organization is well suited for projects that want to limit .cu files to only the necessary parts and integrate CUDA into existing C++ projects.