![]() ![]() Running CUDA-MEMCHECK on your application is easy simply pass your application’s name as a parameter to CUDA-MEMCHECK on the command line. CUDA-MEMCHECK also reports runtime execution errors, identifying situations that could otherwise result in an “unspecified launch failure” error when your application is running. CUDA-MEMCHECK detects these errors in your GPU code and allows you to locate them quickly. cu files.Accurately identifying the source and cause of memory access errors can be frustrating and time-consuming. In this case go to the Edit_Configurations dialog under the Run menu and ensure that clion has not changed the target_executable to be from the cudaNoBuild target.Įdit: Gah! Upon rebuilding the CMake and ide cache after an update to clion 2017.3.3 things are not really working the way they did before. If you suddenly get compilation errors check clion's build target settings - I've noticed that it sometimes mixes and matches the current build settings between the projects. Remember, the cudaNoBuild target is not for building - it will use the c++ toolchain which won't work. add a dependency to clionShadow/CMakeLists.txt at the end of your main CMakeLists.txt with a line like this:.Inline int _syncthreads_count(int predicate) ) This does not have all functions accounted for (missing atomics, math functions (just include math.h for most), texture, surface, timing, warp votie and shuffle, assertion, launch bounds, and video function) #ifdef _JETBRAINS_IDE_ Note that this should have full C++ compatibility, and maybe C. In addition to that I've updated the list to include most intrinsics and values found in the CUDA 8.0 documentation guide. z work properly with out issue, and use grid dim. ![]() I've expanded upon this answer using the method found in this answer to provide a more comprehensive parsing macro, you can now have. It puts a little red line under one character on each end of the launch block, but otherwise treats it as a function call - which is perfectly fine: ![]() CLion even gracefully copes with > syntax. This will get you perfect indexing of virtually all CUDA code. Doing this when you actually build is, obviously, we include them explicitly to make the indexer happy. These headers are all implicitly present when you compile CUDA with clang. This is slightly mental, but gets it to properly index device function calls like _popc and whatever. By pretending to be the CUDA compiler's preprocessor and including device_functions.h, we can get _popc and friends to work, too: #ifdef _JETBRAINS_IDE_ These provide definitions of things like threadIdx. Clang implements the implicitly-defined CUDA builtins by defining them in headers, which it then includes when compiling your code. If you're using Clang's CUDA frontend, you can do better. ![]() One option is to provide endless preprocessor macros (or even struct definitions) for these, but that's ugly and sacrifices type-safety. However, CUDA functions like _syncthreads, _popc will still fail to index. The most minimal solution to this problem is to give clion preprocessor macros to ignore the new keywords, fixing the worst of the brokenness: #ifdef _JETBRAINS_IDE_ Much of the problem is that CLion's parser is derailed by keywords like _host_ or _device_, causing it to fail to do things it otherwise knows how to do:ĬLion has failed to understand Dtype in this example, because the CUDA stuff confused its parsing. You can use this to implement almost complete CUDA support yourself. cuh files as C++ using the File Types settings menu.ĬLion is not able to parse CUDA's language extensions, but it does provide a preprocessor macro that is defined only when clion is parsing the code. First, make sure you tell CLion to treat. ![]()
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