cudaError_enum

Error codes

Values

ValueMeaning
CUDA_SUCCESS0

The API call returned with no errors. In the case of query calls, this can also mean that the operation being queried is complete (see ::cuEventQuery() and ::cuStreamQuery()).

CUDA_ERROR_INVALID_VALUE1

This indicates that one or more of the parameters passed to the API call is not within an acceptable range of values.

CUDA_ERROR_OUT_OF_MEMORY2

The API call failed because it was unable to allocate enough memory to perform the requested operation.

CUDA_ERROR_NOT_INITIALIZED3

This indicates that the CUDA driver has not been initialized with ::cuInit() or that initialization has failed.

CUDA_ERROR_DEINITIALIZED4

This indicates that the CUDA driver is in the process of shutting down.

CUDA_ERROR_PROFILER_DISABLED5

This indicates profiler is not initialized for this run. This can happen when the application is running with external profiling tools like visual profiler.

CUDA_ERROR_PROFILER_NOT_INITIALIZED6

\deprecated This error return is deprecated as of CUDA 5.0. It is no longer an error to attempt to enable/disable the profiling via ::cuProfilerStart or ::cuProfilerStop without initialization.

CUDA_ERROR_PROFILER_ALREADY_STARTED7

\deprecated This error return is deprecated as of CUDA 5.0. It is no longer an error to call cuProfilerStart() when profiling is already enabled.

CUDA_ERROR_PROFILER_ALREADY_STOPPED8

\deprecated This error return is deprecated as of CUDA 5.0. It is no longer an error to call cuProfilerStop() when profiling is already disabled.

CUDA_ERROR_NO_DEVICE100

This indicates that no CUDA-capable devices were detected by the installed CUDA driver.

CUDA_ERROR_INVALID_DEVICE101

This indicates that the device ordinal supplied by the user does not correspond to a valid CUDA device.

CUDA_ERROR_INVALID_IMAGE200

This indicates that the device kernel image is invalid. This can also indicate an invalid CUDA module.

CUDA_ERROR_INVALID_CONTEXT201

This most frequently indicates that there is no context bound to the current thread. This can also be returned if the context passed to an API call is not a valid handle (such as a context that has had ::cuCtxDestroy() invoked on it). This can also be returned if a user mixes different API versions (i.e. 3010 context with 3020 API calls). See ::cuCtxGetApiVersion() for more details.

CUDA_ERROR_CONTEXT_ALREADY_CURRENT202

This indicated that the context being supplied as a parameter to the API call was already the active context. \deprecated This error return is deprecated as of CUDA 3.2. It is no longer an error to attempt to push the active context via ::cuCtxPushCurrent().

CUDA_ERROR_MAP_FAILED205

This indicates that a map or register operation has failed.

CUDA_ERROR_UNMAP_FAILED206

This indicates that an unmap or unregister operation has failed.

CUDA_ERROR_ARRAY_IS_MAPPED207

This indicates that the specified array is currently mapped and thus cannot be destroyed.

CUDA_ERROR_ALREADY_MAPPED208

This indicates that the resource is already mapped.

CUDA_ERROR_NO_BINARY_FOR_GPU209

This indicates that there is no kernel image available that is suitable for the device. This can occur when a user specifies code generation options for a particular CUDA source file that do not include the corresponding device configuration.

CUDA_ERROR_ALREADY_ACQUIRED210

This indicates that a resource has already been acquired.

CUDA_ERROR_NOT_MAPPED211

This indicates that a resource is not mapped.

CUDA_ERROR_NOT_MAPPED_AS_ARRAY212

This indicates that a mapped resource is not available for access as an array.

CUDA_ERROR_NOT_MAPPED_AS_POINTER213

This indicates that a mapped resource is not available for access as a pointer.

CUDA_ERROR_ECC_UNCORRECTABLE214

This indicates that an uncorrectable ECC error was detected during execution.

CUDA_ERROR_UNSUPPORTED_LIMIT215

This indicates that the ::CUlimit passed to the API call is not supported by the active device.

CUDA_ERROR_CONTEXT_ALREADY_IN_USE216

This indicates that the ::CUcontext passed to the API call can only be bound to a single CPU thread at a time but is already bound to a CPU thread.

CUDA_ERROR_PEER_ACCESS_UNSUPPORTED217

This indicates that peer access is not supported across the given devices.

CUDA_ERROR_INVALID_PTX218

This indicates that a PTX JIT compilation failed.

CUDA_ERROR_INVALID_GRAPHICS_CONTEXT219

This indicates an error with OpenGL or DirectX context.

CUDA_ERROR_INVALID_SOURCE300

This indicates that the device kernel source is invalid.

CUDA_ERROR_FILE_NOT_FOUND301

This indicates that the file specified was not found.

CUDA_ERROR_SHARED_OBJECT_SYMBOL_NOT_FOUND302

This indicates that a link to a shared object failed to resolve.

CUDA_ERROR_SHARED_OBJECT_INIT_FAILED303

This indicates that initialization of a shared object failed.

CUDA_ERROR_OPERATING_SYSTEM304

This indicates that an OS call failed.

CUDA_ERROR_INVALID_HANDLE400

This indicates that a resource handle passed to the API call was not valid. Resource handles are opaque types like ::CUstream and ::CUevent.

CUDA_ERROR_NOT_FOUND500

This indicates that a named symbol was not found. Examples of symbols are global/constant variable names, texture names, and surface names.

CUDA_ERROR_NOT_READY600

This indicates that asynchronous operations issued previously have not completed yet. This result is not actually an error, but must be indicated differently than ::CUDA_SUCCESS (which indicates completion). Calls that may return this value include ::cuEventQuery() and ::cuStreamQuery().

CUDA_ERROR_ILLEGAL_ADDRESS700

While executing a kernel, the device encountered a load or store instruction on an invalid memory address. The context cannot be used, so it must be destroyed (and a new one should be created). All existing device memory allocations from this context are invalid and must be reconstructed if the program is to continue using CUDA.

CUDA_ERROR_LAUNCH_OUT_OF_RESOURCES701

This indicates that a launch did not occur because it did not have appropriate resources. This error usually indicates that the user has attempted to pass too many arguments to the device kernel, or the kernel launch specifies too many threads for the kernel's register count. Passing arguments of the wrong size (i.e. a 64-bit pointer when a 32-bit int is expected) is equivalent to passing too many arguments and can also result in this error.

CUDA_ERROR_LAUNCH_TIMEOUT702

This indicates that the device kernel took too long to execute. This can only occur if timeouts are enabled - see the device attribute ::CU_DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT for more information. The context cannot be used (and must be destroyed similar to ::CUDA_ERROR_LAUNCH_FAILED). All existing device memory allocations from this context are invalid and must be reconstructed if the program is to continue using CUDA.

CUDA_ERROR_LAUNCH_INCOMPATIBLE_TEXTURING703

This error indicates a kernel launch that uses an incompatible texturing mode.

CUDA_ERROR_PEER_ACCESS_ALREADY_ENABLED704

This error indicates that a call to ::cuCtxEnablePeerAccess() is trying to re-enable peer access to a context which has already had peer access to it enabled.

CUDA_ERROR_PEER_ACCESS_NOT_ENABLED705

This error indicates that ::cuCtxDisablePeerAccess() is trying to disable peer access which has not been enabled yet via ::cuCtxEnablePeerAccess().

CUDA_ERROR_PRIMARY_CONTEXT_ACTIVE708

This error indicates that the primary context for the specified device has already been initialized.

CUDA_ERROR_CONTEXT_IS_DESTROYED709

This error indicates that the context current to the calling thread has been destroyed using ::cuCtxDestroy, or is a primary context which has not yet been initialized.

CUDA_ERROR_ASSERT710

A device-side assert triggered during kernel execution. The context cannot be used anymore, and must be destroyed. All existing device memory allocations from this context are invalid and must be reconstructed if the program is to continue using CUDA.

CUDA_ERROR_TOO_MANY_PEERS711

This error indicates that the hardware resources required to enable peer access have been exhausted for one or more of the devices passed to ::cuCtxEnablePeerAccess().

CUDA_ERROR_HOST_MEMORY_ALREADY_REGISTERED712

This error indicates that the memory range passed to ::cuMemHostRegister() has already been registered.

CUDA_ERROR_HOST_MEMORY_NOT_REGISTERED713

This error indicates that the pointer passed to ::cuMemHostUnregister() does not correspond to any currently registered memory region.

CUDA_ERROR_HARDWARE_STACK_ERROR714

While executing a kernel, the device encountered a stack error. This can be due to stack corruption or exceeding the stack size limit. The context cannot be used, so it must be destroyed (and a new one should be created). All existing device memory allocations from this context are invalid and must be reconstructed if the program is to continue using CUDA.

CUDA_ERROR_ILLEGAL_INSTRUCTION715

While executing a kernel, the device encountered an illegal instruction. The context cannot be used, so it must be destroyed (and a new one should be created). All existing device memory allocations from this context are invalid and must be reconstructed if the program is to continue using CUDA.

CUDA_ERROR_MISALIGNED_ADDRESS716

While executing a kernel, the device encountered a load or store instruction on a memory address which is not aligned. The context cannot be used, so it must be destroyed (and a new one should be created). All existing device memory allocations from this context are invalid and must be reconstructed if the program is to continue using CUDA.

CUDA_ERROR_INVALID_ADDRESS_SPACE717

While executing a kernel, the device encountered an instruction which can only operate on memory locations in certain address spaces (global, shared, or local), but was supplied a memory address not belonging to an allowed address space. The context cannot be used, so it must be destroyed (and a new one should be created). All existing device memory allocations from this context are invalid and must be reconstructed if the program is to continue using CUDA.

CUDA_ERROR_INVALID_PC718

While executing a kernel, the device program counter wrapped its address space. The context cannot be used, so it must be destroyed (and a new one should be created). All existing device memory allocations from this context are invalid and must be reconstructed if the program is to continue using CUDA.

CUDA_ERROR_LAUNCH_FAILED719

An exception occurred on the device while executing a kernel. Common causes include dereferencing an invalid device pointer and accessing out of bounds shared memory. The context cannot be used, so it must be destroyed (and a new one should be created). All existing device memory allocations from this context are invalid and must be reconstructed if the program is to continue using CUDA.

CUDA_ERROR_NOT_PERMITTED800

This error indicates that the attempted operation is not permitted.

CUDA_ERROR_NOT_SUPPORTED801

This error indicates that the attempted operation is not supported on the current system or device.

CUDA_ERROR_UNKNOWN999

This indicates that an unknown internal error has occurred.

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