Using CUDA and CUDA-Q in a Project

It may be the case that a project that uses CUDA-Q kernels may also want to use CUDA code to do computation on a GPU. This is possible by using both the CUDA Toolkit and CUDA-Q tools. More about programming GPUs in CUDA can be found in the Quick Start Guide.

Once the nvcc compiler is installed, it is possible to write CUDA kernels and have them execute on the system GPU. See NVIDIA’s An Easy Introduction to CUDA C and C++ for more information on getting started with CUDA.

CUDA code uses a unique syntax and is, typically, saved in a file with the extension .cu. For our example, assume we have written our CUDA code in the file

CUDA-Q code is a library-based extension of C++ and uses standard conforming C++ syntax. Typically, a quantum kernel would be saved in a file with the .cpp extension. Again for our example, let’s assume that we’ve written quantum kernels and saved them in the file my_proj_quantum.cpp.

There is a bit of a wrinkle to be aware of before we compile these two compilation units. Version 11 (and earlier) of CUDA nvcc supports the C++ 11, 14, and 17 standards and the default standard is determined by the host C++ compiler. The CUDA-Q compiler, nvq++, defaults to the C++ 20 standard. To get around this limitation, the project makefiles should select a common C++ standard version. Fortunately, nvq++ does allow the use of C++ 17.

Note that starting with version 12 of the CUDA toolkit, the C++ 20 standard is supported.

Our project can then be built with commands such as

nvcc -c -std=c++17 <options> -o my_proj.o
nvq++ -std=c++17 <options> my_project_quantum.cpp my_proj.o -L ${CUDA_INSTALL}/lib64 -lcudart -o my_executable

Above, nvq++ is used for the link step and will make sure the CUDA-Q runtime libraries are linked correctly to the executable program. The CUDA runtime is explicitly added to this command.