#CMAKE FLAGS CODE#

Here's what a correct compiler options line should look like: '$>:-fopenmp>$>:-Xcompiler=-fopenmp>' If you include a target that includes compiler options (flags), most of the time, the options will not be protected by the correct includes (and the chances of them having the correct CUDA wrapper is even smaller). Using targets should work similarly to CXX, but there's a problem. If set to OFF, it will not pass architectures.

If you have a version range that includes 3.18 or newer, you will be using CMAKE_CUDA_ARCHITECTURES variable and the CUDA_ARCHITECTURES property on targets.

In CMake 3.18, it became very easy to target architectures. Finally, you can also generate PTX this will work on all future cards, but will compile just in time. So 7.0 would be a common choice for our 7.2 card. This needs to have the same major number as your target card, and be equal to or less than the target card.

#CMAKE FLAGS CODE#

Then there's a target architecture, which must be equal or greater to the code architecture. You have two choices the first is the code level this will report to the code being compiled a version, like '5.0', and it will take advantage of all the features up to 5.0 but not past (assuming well written code / standard libraries). If you don't, you compile 'ptx', which provide the basic instructions but is compiled at runtime, making it potentially much slower to load.ДЄll cards have an architecture level, like '7.2'. When you build CUDA code, you generally should be targeting an architecture. You can also directly make a PTX file with the CUDA_PTX_COMPILATION property. You can also use separable compilation: set_target_properties(mylib PROPERTIES cu for CUDA files, you can just add libraries exactly like you normally would. This is the easy part as long as you use. You are already used to from the cxx versions. If you are looking for CUDA's standard level, in CMake 3.17 a new collection ofД¬ompiler features were added, like cuda_std_11. Many variables with CXX in the name have a CUDA version with CUDA instead.ДЇor example, to set the C++ standard required for CUDA, if(NOT DEFINED CMAKE_CUDA_STANDARD)

You can check the version withД¬MAKE_CUDA_COMPILER_VERSION. 'NVIDIA', Clang was added in CMake 3.18). You can check variables like CMAKE_CUDA_COMPILER_ID (for nvcc, this is You can see if CUDA is present by checking CMAKE_CUDA_COMPILER (was missing To check to see if CUDA is available, use CheckLanuage: include(CheckLanguage) Want to put this in somewhere conditionally: enable_language(CUDA) You'll probably want CXX listed here also. If CUDA is not optional: project(MY_PROJECT LANGUAGES CUDA CXX) There are two ways to enable CUDA support. Unlike the older languages, CUDA support has been rapidly evolving, and building CUDA is hard, so I would recommend you require a very recent version of CMake! CMake 3.17 and 3.18 have a lot of improvements directly targeting CUDA.ДЄ good resource for CUDA and Modern CMake is this talk by CMake developer Robert Maynard at GTC 2017. The new method, introduced in CMake 3.8 (3.9 for Windows), should be strongly preferred over the old, hacky method - I only mention the old method due to the high chances of an old package somewhere having it. CUDA support is available in two flavors.

Cmake flags