{"slug": "separating-only-cuda-kernels-into-cu-files", "title": "Separating Only CUDA Kernels into `.cu` Files", "summary": "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.", "body_md": "In CUDA programs, you can place only the GPU kernels in `.cu`\n\nfiles while keeping the rest of your code in `.cpp`\n\nfiles.\n\nThis organization confines CUDA-specific code to a limited set of files, making the overall program easier to maintain and understand.\n\nThis example uses the following two files:\n\n```\nkernel.cu\nmain.cpp\n```\n\n`kernel.cu`\n\n: Defines the CUDA kernel.`main.cpp`\n\n: Launches the kernel and handles error checking.In `kernel.cu`\n\n, define only the kernel that runs on the GPU.\n\n```\n__global__ void noopKernel()\n{\n    // A kernel that does nothing\n}\n```\n\nA function marked with `__global__`\n\nis launched from the CPU and executed on the GPU.\n\nThe `noopKernel`\n\nin 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.\n\nIn `main.cpp`\n\n, declare the kernel and launch it with `cudaLaunchKernel`\n\n.\n\n```\n#include <cstdio>\n#include <cstdlib>\n#include <cuda_runtime.h>\n\nvoid noopKernel();\n\nstatic void checkCuda(cudaError_t error, const char* operation)\n{\n    if (error != cudaSuccess) {\n        std::fprintf(\n            stderr,\n            \"%s failed: %s\\n\",\n            operation,\n            cudaGetErrorString(error)\n        );\n        std::exit(EXIT_FAILURE);\n    }\n}\n\nint main()\n{\n    void** kernelArgs = nullptr;\n\n    checkCuda(\n        cudaLaunchKernel(\n            reinterpret_cast<const void*>(&noopKernel),\n            dim3(1, 1, 1),\n            dim3(1, 1, 1),\n            kernelArgs,\n            0,\n            nullptr\n        ),\n        \"cudaLaunchKernel\"\n    );\n\n    checkCuda(cudaGetLastError(), \"kernel launch\");\n\n    checkCuda(\n        cudaDeviceSynchronize(),\n        \"cudaDeviceSynchronize\"\n    );\n\n    std::puts(\"noopKernel completed successfully.\");\n    return EXIT_SUCCESS;\n}\nvoid noopKernel();\n```\n\nAlthough the implementation of `noopKernel`\n\nis in `kernel.cu`\n\n, you still need to declare it so that `main.cpp`\n\ncan reference it.\n\nThe function name and parameter list in the declaration must match the definition in `kernel.cu`\n\n.\n\n`cudaLaunchKernel`\n\n``` js\ncudaLaunchKernel(\n    reinterpret_cast<const void*>(&noopKernel),\n    dim3(1, 1, 1),\n    dim3(1, 1, 1),\n    kernelArgs,\n    0,\n    nullptr\n)\n```\n\nThe main arguments are:\n\n`noopKernel`\n\n: The kernel to launch.`dim3`\n\n: Grid dimensions.`dim3`\n\n: Block dimensions.`kernelArgs`\n\n: Arguments passed to the kernel.`0`\n\n: Size of dynamically allocated shared memory.`nullptr`\n\n: Use the default stream.Since this kernel takes no arguments, `kernelArgs`\n\nis set to `nullptr`\n\n.\n\nThe `checkCuda`\n\nfunction is a helper for checking the return values of CUDA API calls.\n\n``` js\nstatic void checkCuda(cudaError_t error, const char* operation)\n{\n    if (error != cudaSuccess) {\n        std::fprintf(\n            stderr,\n            \"%s failed: %s\\n\",\n            operation,\n            cudaGetErrorString(error)\n        );\n        std::exit(EXIT_FAILURE);\n    }\n}\n```\n\nIf an error occurs, it prints the operation name along with the CUDA error message, then terminates the program.\n\nAfter launching the kernel, errors are checked in two stages:\n\n```\ncheckCuda(cudaGetLastError(), \"kernel launch\");\ncheckCuda(cudaDeviceSynchronize(), \"cudaDeviceSynchronize\");\n```\n\n`cudaGetLastError`\n\nprimarily detects errors related to the kernel launch configuration.\n\n`cudaDeviceSynchronize`\n\nwaits until the GPU has finished executing and reports any errors that occurred during kernel execution.\n\nFirst, compile each source file into an object file.\n\n```\nnvcc -c kernel.cu\ng++ -c main.cpp\n```\n\nThis produces:\n\n```\nkernel.o\nmain.o\n```\n\nFinally, link the object files using `nvcc`\n\n.\n\n```\nnvcc kernel.o main.o -o app\n```\n\nRun the program with:\n\n```\n./app\n```\n\nIf execution succeeds, the following message is displayed:\n\n```\nnoopKernel completed successfully.\n```\n\nSeparating kernels into `.cu`\n\nfiles provides several advantages:\n\n`g++`\n\nor other standard C++ compilers.This organization is well suited for projects where you want to limit the use of `.cu`\n\nfiles to only the necessary parts and keep CUDA kernels clearly separated from the rest of the C++ code.", "url": "https://wpnews.pro/news/separating-only-cuda-kernels-into-cu-files", "canonical_source": "https://dev.to/vast-cow/separating-only-cuda-kernels-into-cu-files-5dfp", "published_at": "2026-07-16 11:06:32+00:00", "updated_at": "2026-07-16 11:34:22.897862+00:00", "lang": "en", "topics": ["developer-tools"], "entities": ["CUDA", "nvcc", "g++"], "alternates": {"html": "https://wpnews.pro/news/separating-only-cuda-kernels-into-cu-files", "markdown": "https://wpnews.pro/news/separating-only-cuda-kernels-into-cu-files.md", "text": "https://wpnews.pro/news/separating-only-cuda-kernels-into-cu-files.txt", "jsonld": "https://wpnews.pro/news/separating-only-cuda-kernels-into-cu-files.jsonld"}}