通常,cuda_include_dirs是CUDA头文件所在的目录,例如/usr/local/cuda/include。 cuda_cudart_library是CUDA运行时库文件所在的路径,例如/usr/local/cuda/lib64/libcudart.so。 如果这些路径与你的CUDA安装路径不符,你可能需要在CMakeLists.txt文件中手动指定它们。 在CMake中配置CUDA: 如果你的项目使用CMake进行构...
CMakeLists.txt中有找CUDA的代码(例如find_package(CUDA REQUIRED)),系统也有装完整的NVIDIA驱动和CUDA环境(nvidia-smi能正常输出CUDA版本),但CMake编译报错: Could NOT find CUDA (missing: CUDA_INCLUDE_DIRS CUDA_CUDART_LIBRARY) 查看发现系统的cuda路径是/usr/local/cuda-11.2/,而CMake默认搜索CUDA的路径是/u...
Hello, I am using ROS2 ELOQUENT and i am trying to build zed-ros2-wrapper and i get the below error.attached the image. I have tried with both master branch and eloquent branch but i get the same error. Please support! OS: Ubuntu OS 18.0...
设置LD_LIBRARY_PATH环境变量:确保LD_LIBRARY_PATH包含CUDA库路径。 exportLD_LIBRARY_PATH=/usr/local/cuda/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}} 1. 在Makefile中配置链接选项:在Makefile中明确指定链接选项,确保正确链接CUDA库。 LDFLAGS:=-L/usr/local/cuda/lib64 -lcudart -lcublas -lcurand ...
include_directories(${CUDA_INCLUDE_DIRS}) link_directories(${CUDA_LIBRARIES}) add_executable(my_program my_program.cu) target_link_libraries(my_program ${CUDA_LIBRARIES}) 通过以上方法,可以有效解决在Linux系统中编译器配置问题,确保CUDA程序的正确编译和高效运行。
Include the curand_kernel.h header in your project files to use the device API and, as with the host API, link to the curand.lib library in your project's properties. The generation occurs within kernels, so (unlike the host API) you will need to use the CUDA Build Customization to ...
-- Found AMD library: /usr/lib/libamd.so -- Found CAMD headers in: /usr/include/suitesparse -- Found CAMD library: /usr/lib/libcamd.so -- Found CCOLAMD headers in: /usr/include/suitesparse -- Found CCOLAMD library: /usr/lib/libccolamd.so ...
export LD_LIBRARY_PATH=/usr/local/cuda/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}} 在Makefile中配置链接选项:在Makefile中明确指定链接选项,确保正确链接CUDA库。 LDFLAGS := -L/usr/local/cuda/lib64 -lcudart -lcublas -lcurand 编译过程中的常见错误 问题描述 未定义引用错误:编译时出现未定义...
D:\cuda\NVIDIA GPU Computing Toolkit\CUDA\v10.2\include D:\cuda\NVIDIA GPU Computing Toolkit\CUDA\v10.2\lib D:\cuda\NVIDIA GPU Computing Toolkit\CUDA\v10.2\libnvvp 1. 2. 3. 4. 3、验证安装是否成功 配置完成后,我们可以验证是否配置成功,主要使用CUDA内置的deviceQuery.exe 和 bandwithTest.exe: ...
(CUDA_PATH)/lib64 -lcublas -lcufft -lcudartCUDA_INC+=-I$(CUDA_PATH)/includeCFLAGS+=-std=c99INCLUDES:=# specify include path for host codeGPU_CARD:=-arch=sm_35# specify your device compute capabilityNVCC_FLAGS+=-O3 -dc# separate compilationNVCC_FLAGS+=-Xcompiler -fopenmpCUDA_LINK_FLAGS...