Separating Only CUDA Kernels into `.cu` Files

A developer demonstrated how to separate CUDA kernels into .cu files while keeping the rest of the code in .cpp files, using a minimal noopKernel example. The approach confines CUDA-specific code to a limited set of files, improving maintainability. The example includes compilation with nvcc and g++ and linking with nvcc.

In CUDA programs, you can place only the GPU kernels in .cu files while keeping the rest of your code in .cpp files. This organization confines CUDA-specific code to a limited set of files, making the overall program easier to maintain and understand. This example uses the following two files: kernel.cu main.cpp kernel.cu : Defines the CUDA kernel. main.cpp : Launches the kernel and handles error checking.In kernel.cu , define only the kernel that runs on the GPU. global void noopKernel { // A kernel that does nothing } A function marked with global is launched from the CPU and executed on the GPU. The noopKernel in this example performs no work. It is the smallest possible example for verifying that a kernel can be separated into its own file and launched successfully. In main.cpp , declare the kernel and launch it with cudaLaunchKernel . include <cstdio include <cstdlib include <cuda runtime.h void noopKernel ; static void checkCuda cudaError t error, const char operation { if error = cudaSuccess { std::fprintf stderr, "%s failed: %s\n", operation, cudaGetErrorString error ; std::exit EXIT FAILURE ; } } int main { void kernelArgs = nullptr; checkCuda cudaLaunchKernel reinterpret cast<const void &noopKernel , dim3 1, 1, 1 , dim3 1, 1, 1 , kernelArgs, 0, nullptr , "cudaLaunchKernel" ; checkCuda cudaGetLastError , "kernel launch" ; checkCuda cudaDeviceSynchronize , "cudaDeviceSynchronize" ; std::puts "noopKernel completed successfully." ; return EXIT SUCCESS; } void noopKernel ; Although the implementation of noopKernel is in kernel.cu , you still need to declare it so that main.cpp can reference it. The function name and parameter list in the declaration must match the definition in kernel.cu . cudaLaunchKernel js cudaLaunchKernel reinterpret cast<const void &noopKernel , dim3 1, 1, 1 , dim3 1, 1, 1 , kernelArgs, 0, nullptr The main arguments are: noopKernel : The kernel to launch. dim3 : Grid dimensions. dim3 : Block dimensions. kernelArgs : Arguments passed to the kernel. 0 : Size of dynamically allocated shared memory. nullptr : Use the default stream.Since this kernel takes no arguments, kernelArgs is set to nullptr . The checkCuda function is a helper for checking the return values of CUDA API calls. js static void checkCuda cudaError t error, const char operation { if error = cudaSuccess { std::fprintf stderr, "%s failed: %s\n", operation, cudaGetErrorString error ; std::exit EXIT FAILURE ; } } If an error occurs, it prints the operation name along with the CUDA error message, then terminates the program. After launching the kernel, errors are checked in two stages: checkCuda cudaGetLastError , "kernel launch" ; checkCuda cudaDeviceSynchronize , "cudaDeviceSynchronize" ; cudaGetLastError primarily detects errors related to the kernel launch configuration. cudaDeviceSynchronize waits until the GPU has finished executing and reports any errors that occurred during kernel execution. First, compile each source file into an object file. nvcc -c kernel.cu g++ -c main.cpp This produces: kernel.o main.o Finally, link the object files using nvcc . nvcc kernel.o main.o -o app Run the program with: ./app If execution succeeds, the following message is displayed: noopKernel completed successfully. Separating kernels into .cu files provides several advantages: g++ or other standard C++ compilers.This organization is well suited for projects where you want to limit the use of .cu files to only the necessary parts and keep CUDA kernels clearly separated from the rest of the C++ code.