/CMake 3.19


Deprecated since version 3.10: Superseded by first-class support for the CUDA language in CMake. Superseded by the FindCUDAToolkit for CUDA toolkit libraries.


It is no longer necessary to use this module or call find_package(CUDA) for compiling CUDA code. Instead, list CUDA among the languages named in the top-level call to the project() command, or call the enable_language() command with CUDA. Then one can add CUDA (.cu) sources to programs directly in calls to add_library() and add_executable().

To find and use the CUDA toolkit libraries the FindCUDAToolkit module has superseded this module. It works whether or not the CUDA language is enabled.

Documentation of Deprecated Usage

Tools for building CUDA C files: libraries and build dependencies.

This script locates the NVIDIA CUDA C tools. It should work on Linux, Windows, and macOS and should be reasonably up to date with CUDA C releases.

This script makes use of the standard find_package() arguments of <VERSION>, REQUIRED and QUIET. CUDA_FOUND will report if an acceptable version of CUDA was found.

The script will prompt the user to specify CUDA_TOOLKIT_ROOT_DIR if the prefix cannot be determined by the location of nvcc in the system path and REQUIRED is specified to find_package(). To use a different installed version of the toolkit set the environment variable CUDA_BIN_PATH before running cmake (e.g. CUDA_BIN_PATH=/usr/local/cuda1.0 instead of the default /usr/local/cuda) or set CUDA_TOOLKIT_ROOT_DIR after configuring. If you change the value of CUDA_TOOLKIT_ROOT_DIR, various components that depend on the path will be relocated.

It might be necessary to set CUDA_TOOLKIT_ROOT_DIR manually on certain platforms, or to use a CUDA runtime not installed in the default location. In newer versions of the toolkit the CUDA library is included with the graphics driver – be sure that the driver version matches what is needed by the CUDA runtime version.

The following variables affect the behavior of the macros in the script (in alphabetical order). Note that any of these flags can be changed multiple times in the same directory before calling CUDA_ADD_EXECUTABLE, CUDA_ADD_LIBRARY, CUDA_COMPILE, CUDA_COMPILE_PTX, CUDA_COMPILE_FATBIN, CUDA_COMPILE_CUBIN or CUDA_WRAP_SRCS:

CUDA_64_BIT_DEVICE_CODE (Default matches host bit size)
-- Set to ON to compile for 64 bit device code, OFF for 32 bit device code.
   Note that making this different from the host code when generating object
   or C files from CUDA code just won't work, because size_t gets defined by
   nvcc in the generated source.  If you compile to PTX and then load the
   file yourself, you can mix bit sizes between device and host.

-- Set to ON if you want the custom build rule to be attached to the source
   file in Visual Studio.  Turn OFF if you add the same cuda file to multiple

   This allows the user to build the target from the CUDA file; however, bad
   things can happen if the CUDA source file is added to multiple targets.
   When performing parallel builds it is possible for the custom build
   command to be run more than once and in parallel causing cryptic build
   errors.  VS runs the rules for every source file in the target, and a
   source can have only one rule no matter how many projects it is added to.
   When the rule is run from multiple targets race conditions can occur on
   the generated file.  Eventually everything will get built, but if the user
   is unaware of this behavior, there may be confusion.  It would be nice if
   this script could detect the reuse of source files across multiple targets
   and turn the option off for the user, but no good solution could be found.

-- Set to ON to enable and extra compilation pass with the -cubin option in
   Device mode. The output is parsed and register, shared memory usage is
   printed during build.

CUDA_BUILD_EMULATION (Default OFF for device mode)
-- Set to ON for Emulation mode. -D_DEVICEEMU is defined for CUDA C files

 -- The <PRIVATE|PUBLIC|INTERFACE> keyword to use for internal
    target_link_libraries calls. The default is to use no keyword which
    uses the old "plain" form of target_link_libraries. Note that is matters
    because whatever is used inside the FindCUDA module must also be used
    outside - the two forms of target_link_libraries cannot be mixed.

-- Set to the path you wish to have the generated files placed.  If it is
   blank output files will be placed in CMAKE_CURRENT_BINARY_DIR.
   Intermediate files will always be placed in

-- Set to OFF for C compilation of host code.

-- Set the host compiler to be used by nvcc.  Ignored if -ccbin or
   --compiler-bindir is already present in the CUDA_NVCC_FLAGS or
   CUDA_NVCC_FLAGS_<CONFIG> variables.  For Visual Studio targets,
   the host compiler is constructed with one or more visual studio macros
   such as $(VCInstallDir), that expands out to the path when
   the command is run from within VS.
   If the CUDAHOSTCXX environment variable is set it will
   be used as the default.

-- Additional NVCC command line arguments.  NOTE: multiple arguments must be
   semi-colon delimited (e.g. --compiler-options;-Wall)

-- Set to ON to propagate CMAKE_{C,CXX}_FLAGS and their configuration
   dependent counterparts (e.g. CMAKE_C_FLAGS_DEBUG) automatically to the
   host compiler through nvcc's -Xcompiler flag.  This helps make the
   generated host code match the rest of the system better.  Sometimes
   certain flags give nvcc problems, and this will help you turn the flag
   propagation off.  This does not affect the flags supplied directly to nvcc
   via CUDA_NVCC_FLAGS or through the OPTION flags specified through
   shared library compilation are not affected by this flag.

-- If set this will enable separable compilation for all CUDA runtime object
   files.  If used outside of CUDA_ADD_EXECUTABLE and CUDA_ADD_LIBRARY
   (e.g. calling CUDA_WRAP_SRCS directly),

-- If this source file property is set, it can override the format specified
   to CUDA_WRAP_SRCS (OBJ, PTX, CUBIN, or FATBIN).  If an input source file
   is not a .cu file, setting this file will cause it to be treated as a .cu
   file. See documentation for set_source_files_properties on how to set
   this property.

-- When enabled the static version of the CUDA runtime library will be used
   in CUDA_LIBRARIES.  If the version of CUDA configured doesn't support
   this option, then it will be silently disabled.

-- Set to ON to see all the commands used when building the CUDA file.  When
   using a Makefile generator the value defaults to VERBOSE (run make
   VERBOSE=1 to see output), although setting CUDA_VERBOSE_BUILD to ON will
   always print the output.

The script creates the following macros (in alphabetical order):

-- Adds the cufft library to the target (can be any target).  Handles whether
   you are in emulation mode or not.

-- Adds the cublas library to the target (can be any target).  Handles
   whether you are in emulation mode or not.

CUDA_ADD_EXECUTABLE( cuda_target file0 file1 ...
                     [WIN32] [MACOSX_BUNDLE] [EXCLUDE_FROM_ALL] [OPTIONS ...] )
-- Creates an executable "cuda_target" which is made up of the files
   specified.  All of the non CUDA C files are compiled using the standard
   build rules specified by CMAKE and the cuda files are compiled to object
   files using nvcc and the host compiler.  In addition CUDA_INCLUDE_DIRS is
   added automatically to include_directories().  Some standard CMake target
   calls can be used on the target after calling this macro
   (e.g. set_target_properties and target_link_libraries), but setting
   properties that adjust compilation flags will not affect code compiled by
   nvcc.  Such flags should be modified before calling CUDA_ADD_EXECUTABLE,

CUDA_ADD_LIBRARY( cuda_target file0 file1 ...
                  [STATIC | SHARED | MODULE] [EXCLUDE_FROM_ALL] [OPTIONS ...] )
-- Same as CUDA_ADD_EXECUTABLE except that a library is created.

-- Creates a convenience target that deletes all the dependency files
   generated.  You should make clean after running this target to ensure the
   dependency files get regenerated.

CUDA_COMPILE( generated_files file0 file1 ... [STATIC | SHARED | MODULE]
              [OPTIONS ...] )
-- Returns a list of generated files from the input source files to be used

CUDA_COMPILE_PTX( generated_files file0 file1 ... [OPTIONS ...] )
-- Returns a list of PTX files generated from the input source files.

CUDA_COMPILE_FATBIN( generated_files file0 file1 ... [OPTIONS ...] )
-- Returns a list of FATBIN files generated from the input source files.

CUDA_COMPILE_CUBIN( generated_files file0 file1 ... [OPTIONS ...] )
-- Returns a list of CUBIN files generated from the input source files.

                                                     object_files )
-- Compute the name of the intermediate link file used for separable
   compilation.  This file name is typically passed into
   CUDA_LINK_SEPARABLE_COMPILATION_OBJECTS.  output_file_var is produced
   based on cuda_target the list of objects files that need separable
   compilation as specified by object_files.  If the object_files list is
   empty, then output_file_var will be empty.  This function is called
   automatically for CUDA_ADD_LIBRARY and CUDA_ADD_EXECUTABLE.  Note that
   this is a function and not a macro.

-- Sets the directories that should be passed to nvcc
   (e.g. nvcc -Ipath0 -Ipath1 ... ). These paths usually contain other .cu

                                         nvcc_flags object_files)
-- Generates the link object required by separable compilation from the given
   object files.  This is called automatically for CUDA_ADD_EXECUTABLE and
   CUDA_ADD_LIBRARY, but can be called manually when using CUDA_WRAP_SRCS
   directly.  When called from CUDA_ADD_LIBRARY or CUDA_ADD_EXECUTABLE the
   nvcc_flags passed in are the same as the flags passed in via the OPTIONS
   argument.  The only nvcc flag added automatically is the bitness flag as
   specified by CUDA_64_BIT_DEVICE_CODE.  Note that this is a function
   instead of a macro.

CUDA_SELECT_NVCC_ARCH_FLAGS(out_variable [target_CUDA_architectures])
-- Selects GPU arch flags for nvcc based on target_CUDA_architectures
   target_CUDA_architectures : Auto | Common | All | LIST(ARCH_AND_PTX ...)
    - "Auto" detects local machine GPU compute arch at runtime.
    - "Common" and "All" cover common and entire subsets of architectures
   NAME: Fermi Kepler Maxwell Kepler+Tegra Kepler+Tesla Maxwell+Tegra Pascal
   NUM: Any number. Only those pairs are currently accepted by NVCC though:
         2.0 2.1 3.0 3.2 3.5 3.7 5.0 5.2 5.3 6.0 6.2
   Returns LIST of flags to be added to CUDA_NVCC_FLAGS in ${out_variable}
   Additionally, sets ${out_variable}_readable to the resulting numeric list
    CUDA_SELECT_NVCC_ARCH_FLAGS(ARCH_FLAGS 3.0 3.5+PTX 5.2(5.0) Maxwell)

   More info on CUDA architectures: https://en.wikipedia.org/wiki/CUDA
   Note that this is a function instead of a macro.

CUDA_WRAP_SRCS ( cuda_target format generated_files file0 file1 ...
                 [STATIC | SHARED | MODULE] [OPTIONS ...] )
-- This is where all the magic happens.  CUDA_ADD_EXECUTABLE,
   function under the hood.

   Given the list of files (file0 file1 ... fileN) this macro generates
   custom commands that generate either PTX or linkable objects (use "PTX" or
   "OBJ" for the format argument to switch).  Files that don't end with .cu
   or have the HEADER_FILE_ONLY property are ignored.

   The arguments passed in after OPTIONS are extra command line options to
   give to nvcc.  You can also specify per configuration options by
   specifying the name of the configuration followed by the options.  General
   options must precede configuration specific options.  Not all
   configurations need to be specified, only the ones provided will be used.

      OPTIONS -DFLAG=2 "-DFLAG_OTHER=space in flag"
      DEBUG -g
      RELEASE --use_fast_math
      RELWITHDEBINFO --use_fast_math;-g
      MINSIZEREL --use_fast_math

   For certain configurations (namely VS generating object files with
   CUDA_ATTACH_VS_BUILD_RULE_TO_CUDA_FILE set to ON), no generated file will
   be produced for the given cuda file.  This is because when you add the
   cuda file to Visual Studio it knows that this file produces an object file
   and will link in the resulting object file automatically.

   This script will also generate a separate cmake script that is used at
   build time to invoke nvcc.  This is for several reasons.

     1. nvcc can return negative numbers as return values which confuses
     Visual Studio into thinking that the command succeeded.  The script now
     checks the error codes and produces errors when there was a problem.

     2. nvcc has been known to not delete incomplete results when it
     encounters problems.  This confuses build systems into thinking the
     target was generated when in fact an unusable file exists.  The script
     now deletes the output files if there was an error.

     3. By putting all the options that affect the build into a file and then
     make the build rule dependent on the file, the output files will be
     regenerated when the options change.

   This script also looks at optional arguments STATIC, SHARED, or MODULE to
   determine when to target the object compilation for a shared library.
   BUILD_SHARED_LIBS is ignored in CUDA_WRAP_SRCS, but it is respected in
   CUDA_ADD_LIBRARY.  On some systems special flags are added for building
   objects intended for shared libraries.  A preprocessor macro,
   <target_name>_EXPORTS is defined when a shared library compilation is

   Flags passed into add_definitions with -D or /D are passed along to nvcc.

The script defines the following variables:

CUDA_VERSION_MAJOR    -- The major version of cuda as reported by nvcc.
CUDA_VERSION_MINOR    -- The minor version.
CUDA_HAS_FP16         -- Whether a short float (float16,fp16) is supported.

CUDA_TOOLKIT_ROOT_DIR -- Path to the CUDA Toolkit (defined if not set).
CUDA_SDK_ROOT_DIR     -- Path to the CUDA SDK.  Use this to find files in the
                         SDK.  This script will not directly support finding
                         specific libraries or headers, as that isn't
                         supported by NVIDIA.  If you want to change
                         libraries when the path changes see the
                         FindCUDA.cmake script for an example of how to clear
                         these variables.  There are also examples of how to
                         use the CUDA_SDK_ROOT_DIR to locate headers or
                         libraries, if you so choose (at your own risk).
CUDA_INCLUDE_DIRS     -- Include directory for cuda headers.  Added automatically
                         for CUDA_ADD_EXECUTABLE and CUDA_ADD_LIBRARY.
CUDA_LIBRARIES        -- Cuda RT library.
CUDA_CUFFT_LIBRARIES  -- Device or emulation library for the Cuda FFT
                         implementation (alternative to:
                         CUDA_ADD_CUFFT_TO_TARGET macro)
CUDA_CUBLAS_LIBRARIES -- Device or emulation library for the Cuda BLAS
                         implementation (alternative to:
                         CUDA_ADD_CUBLAS_TO_TARGET macro).
CUDA_cudart_static_LIBRARY -- Statically linkable cuda runtime library.
                              Only available for CUDA version 5.5+
CUDA_cudadevrt_LIBRARY -- Device runtime library.
                          Required for separable compilation.
CUDA_cupti_LIBRARY    -- CUDA Profiling Tools Interface library.
                         Only available for CUDA version 4.0+.
CUDA_curand_LIBRARY   -- CUDA Random Number Generation library.
                         Only available for CUDA version 3.2+.
CUDA_cusolver_LIBRARY -- CUDA Direct Solver library.
                         Only available for CUDA version 7.0+.
CUDA_cusparse_LIBRARY -- CUDA Sparse Matrix library.
                         Only available for CUDA version 3.2+.
CUDA_npp_LIBRARY      -- NVIDIA Performance Primitives lib.
                         Only available for CUDA version 4.0+.
CUDA_nppc_LIBRARY     -- NVIDIA Performance Primitives lib (core).
                         Only available for CUDA version 5.5+.
CUDA_nppi_LIBRARY     -- NVIDIA Performance Primitives lib (image processing).
                         Only available for CUDA version 5.5 - 8.0.
CUDA_nppial_LIBRARY   -- NVIDIA Performance Primitives lib (image processing).
                         Only available for CUDA version 9.0.
CUDA_nppicc_LIBRARY   -- NVIDIA Performance Primitives lib (image processing).
                         Only available for CUDA version 9.0.
CUDA_nppicom_LIBRARY  -- NVIDIA Performance Primitives lib (image processing).
                         Only available for CUDA version 9.0 - 10.2.
                         Replaced by nvjpeg.
CUDA_nppidei_LIBRARY  -- NVIDIA Performance Primitives lib (image processing).
                         Only available for CUDA version 9.0.
CUDA_nppif_LIBRARY    -- NVIDIA Performance Primitives lib (image processing).
                         Only available for CUDA version 9.0.
CUDA_nppig_LIBRARY    -- NVIDIA Performance Primitives lib (image processing).
                         Only available for CUDA version 9.0.
CUDA_nppim_LIBRARY    -- NVIDIA Performance Primitives lib (image processing).
                         Only available for CUDA version 9.0.
CUDA_nppist_LIBRARY   -- NVIDIA Performance Primitives lib (image processing).
                         Only available for CUDA version 9.0.
CUDA_nppisu_LIBRARY   -- NVIDIA Performance Primitives lib (image processing).
                         Only available for CUDA version 9.0.
CUDA_nppitc_LIBRARY   -- NVIDIA Performance Primitives lib (image processing).
                         Only available for CUDA version 9.0.
CUDA_npps_LIBRARY     -- NVIDIA Performance Primitives lib (signal processing).
                         Only available for CUDA version 5.5+.
CUDA_nvcuvenc_LIBRARY -- CUDA Video Encoder library.
                         Only available for CUDA version 3.2+.
                         Windows only.
CUDA_nvcuvid_LIBRARY  -- CUDA Video Decoder library.
                         Only available for CUDA version 3.2+.
                         Windows only.
                      -- NVIDA CUDA Tools Extension library.
                         Available for CUDA version 5+.
                         Available for CUDA version 5+.

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