整理下NVIDIA官方文档中列的CUDA常见错误类型。
参考资料：CUDA官方文档
cudaSuccess = 0
The API call returned with no errors. In the case of query calls, this also means that the operation being queried is complete (see cudaEventQuery() and cudaStreamQuery()).
cudaErrorInvalidValue = 1
This indicates that one or more of the parameters passed to the API call is not within an acceptable range of values.
cudaErrorMemoryAllocation = 2
The API call failed because it was unable to allocate enough memory to perform the requested operation.
cudaErrorInitializatiOnError= 3
The API call failed because the CUDA driver and runtime could not be initialized.
cudaErrorCudartUnloading = 4
This indicates that a CUDA Runtime API call cannot be executed because it is being called during process shut down, at a point in time after CUDA driver has been unloaded.
cudaErrorProfilerDisabled = 5
This indicates profiler is not initialized for this run. This can happen when the application is running with external profiling tools like visual profiler.
cudaErrorProfilerNotInitialized = 6
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 cudaProfilerStart or cudaProfilerStop without initialization.
cudaErrorProfilerAlreadyStarted = 7
Deprecated
This error return is deprecated as of CUDA 5.0. It is no longer an error to call cudaProfilerStart() when profiling is already enabled.
cudaErrorProfilerAlreadyStopped = 8
Deprecated
This error return is deprecated as of CUDA 5.0. It is no longer an error to call cudaProfilerStop() when profiling is already disabled.
cudaErrorInvalidCOnfiguration= 9
This indicates that a kernel launch is requesting resources that can never be satisfied by the current device. Requesting more shared memory per block than the device supports will trigger this error, as will requesting too many threads or blocks. See cudaDeviceProp for more device limitations.
cudaErrorInvalidPitchValue = 12
This indicates that one or more of the pitch-related parameters passed to the API call is not within the acceptable range for pitch.
cudaErrorInvalidSymbol = 13
This indicates that the symbol name/identifier passed to the API call is not a valid name or identifier.
cudaErrorInvalidHostPointer = 16
Deprecated
This error return is deprecated as of CUDA 10.1.
This indicates that at least one host pointer passed to the API call is not a valid host pointer.
This indicates that at least one device pointer passed to the API call is not a valid device pointer.
This indicated that the user has taken the address of a constant variable, which was forbidden up until the CUDA 3.1 release.
This indicated that a texture fetch was not able to be performed. This was previously used for device emulation of texture operations.
This indicated that a texture was not bound for access. This was previously used for device emulation of texture operations.
This indicated that a synchronization operation had failed. This was previously used for some device emulation functions.
Mixing of device and device emulation code was not allowed.
This indicates that the API call is not yet implemented. Production releases of CUDA will never return this error.
This indicated that an emulated device pointer exceeded the 32-bit address range.
cudaErrorStubLibrary = 34
This indicates that the CUDA driver that the application has loaded is a stub library. Applications that run with the stub rather than a real driver loaded will result in CUDA API returning this error.
cudaErrorInsufficientDriver = 35
This indicates that the installed NVIDIA CUDA driver is older than the CUDA runtime library. This is not a supported configuration. Users should install an updated NVIDIA display driver to allow the application to run.
cudaErrorCallRequiresNewerDriver = 36
This indicates that the API call requires a newer CUDA driver than the one currently installed. Users should install an updated NVIDIA CUDA driver to allow the API call to succeed.
cudaErrorInvalidSurface = 37
This indicates that the surface passed to the API call is not a valid surface.
cudaErrorDuplicateVariableName = 43
This indicates that multiple global or constant variables (across separate CUDA source files in the application) share the same string name.
cudaErrorDuplicateTextureName = 44
This indicates that multiple textures (across separate CUDA source files in the application) share the same string name.
cudaErrorDuplicateSurfaceName = 45
This indicates that multiple surfaces (across separate CUDA source files in the application) share the same string name.
cudaErrorDevicesUnavailable = 46
This indicates that all CUDA devices are busy or unavailable at the current time. Devices are often busy/unavailable due to use of cudaComputeModeExclusive, cudaComputeModeProhibited or when long running CUDA kernels have filled up the GPU and are blocking new work from starting. They can also be unavailable due to memory constraints on a device that already has active CUDA work being performed.
cudaErrorIncompatibleDriverCOntext= 49
This indicates that the current context is not compatible with this the CUDA Runtime. This can only occur if you are using CUDA Runtime/Driver interoperability and have created an existing Driver context using the driver API. The Driver context may be incompatible either because the Driver context was created using an older version of the API, because the Runtime API call expects a primary driver context and the Driver context is not primary, or because the Driver context has been destroyed. Please see Interactions with the CUDA Driver API" for more information.
cudaErrorMissingCOnfiguration= 52
The device function being invoked (usually via cudaLaunchKernel()) was not previously configured via the cudaConfigureCall() function.
cudaErrorPriorLaunchFailure = 53
Deprecated
This error return is deprecated as of CUDA 3.1. Device emulation mode was removed with the CUDA 3.1 release.
This indicated that a previous kernel launch failed. This was previously used for device emulation of kernel launches.
cudaErrorLaunchMaxDepthExceeded = 65
This error indicates that a device runtime grid launch did not occur because the depth of the child grid would exceed the maximum supported number of nested grid launches.
cudaErrorLaunchFileScopedTex = 66
This error indicates that a grid launch did not occur because the kernel uses file-scoped textures which are unsupported by the device runtime. Kernels launched via the device runtime only support textures created with the Texture Object API’s.
cudaErrorLaunchFileScopedSurf = 67
This error indicates that a grid launch did not occur because the kernel uses file-scoped surfaces which are unsupported by the device runtime. Kernels launched via the device runtime only support surfaces created with the Surface Object API’s.
cudaErrorSyncDepthExceeded = 68
This error indicates that a call to cudaDeviceSynchronize made from the device runtime failed because the call was made at grid depth greater than than either the default (2 levels of grids) or user specified device limit cudaLimitDevRuntimeSyncDepth. To be able to synchronize on launched grids at a greater depth successfully, the maximum nested depth at which cudaDeviceSynchronize will be called must be specified with the cudaLimitDevRuntimeSyncDepth limit to the cudaDeviceSetLimit api before the host-side launch of a kernel using the device runtime. Keep in mind that additional levels of sync depth require the runtime to reserve large amounts of device memory that cannot be used for user allocations.
cudaErrorLaunchPendingCountExceeded = 69
This error indicates that a device runtime grid launch failed because the launch would exceed the limit cudaLimitDevRuntimePendingLaunchCount. For this launch to proceed successfully, cudaDeviceSetLimit must be called to set the cudaLimitDevRuntimePendingLaunchCount to be higher than the upper bound of outstanding launches that can be issued to the device runtime. Keep in mind that raising the limit of pending device runtime launches will require the runtime to reserve device memory that cannot be used for user allocations.
cudaErrorInvalidDeviceFunction = 98
The requested device function does not exist or is not compiled for the proper device architecture.
cudaErrorNoDevice = 100
This indicates that no CUDA-capable devices were detected by the installed CUDA driver.
cudaErrorInvalidDevice = 101
This indicates that the device ordinal supplied by the user does not correspond to a valid CUDA device.
cudaErrorDeviceNotLicensed = 102
This indicates that the device doesn’t have a valid Grid License.
cudaErrorSoftwareValidityNotEstablished = 103
By default, the CUDA runtime may perform a minimal set of self-tests, as well as CUDA driver tests, to establish the validity of both. Introduced in CUDA 11.2, this error return indicates that at least one of these tests has failed and the validity of either the runtime or the driver could not be established.
cudaErrorStartupFailure = 127
This indicates an internal startup failure in the CUDA runtime.
cudaErrorInvalidKernelImage = 200
This indicates that the device kernel image is invalid.
cudaErrorDeviceUninitialized = 201
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.
cudaErrorMapBufferObjectFailed = 205
This indicates that the buffer object could not be mapped.
cudaErrorUnmapBufferObjectFailed = 206
This indicates that the buffer object could not be unmapped.
cudaErrorArrayIsMapped = 207
This indicates that the specified array is currently mapped and thus cannot be destroyed.
cudaErrorAlreadyMapped = 208
This indicates that the resource is already mapped.
cudaErrorNoKernelImageForDevice = 209
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.
cudaErrorAlreadyAcquired = 210
This indicates that a resource has already been acquired.
cudaErrorNotMapped = 211
This indicates that a resource is not mapped.
cudaErrorNotMappedAsArray = 212
This indicates that a mapped resource is not available for access as an array.
cudaErrorNotMappedAsPointer = 213
This indicates that a mapped resource is not available for access as a pointer.
cudaErrorECCUncorrectable = 214
This indicates that an uncorrectable ECC error was detected during execution.
cudaErrorUnsupportedLimit = 215
This indicates that the cudaLimit passed to the API call is not supported by the active device.
cudaErrorDeviceAlreadyInUse = 216
This indicates that a call tried to access an exclusive-thread device that is already in use by a different thread.
cudaErrorPeerAccessUnsupported = 217
This error indicates that P2P access is not supported across the given devices.
cudaErrorInvalidPtx = 218
A PTX compilation failed. The runtime may fall back to compiling PTX if an application does not contain a suitable binary for the current device.
cudaErrorInvalidGraphicsCOntext= 219
This indicates an error with the OpenGL or DirectX context.
cudaErrorNvlinkUncorrectable = 220
This indicates that an uncorrectable NVLink error was detected during the execution.
cudaErrorJitCompilerNotFound = 221
This indicates that the PTX JIT compiler library was not found. The JIT Compiler library is used for PTX compilation. The runtime may fall back to compiling PTX if an application does not contain a suitable binary for the current device.
cudaErrorUnsupportedPtxVersion = 222
This indicates that the provided PTX was compiled with an unsupported toolchain. The most common reason for this, is the PTX was generated by a compiler newer than what is supported by the CUDA driver and PTX JIT compiler.
cudaErrorJitCompilatiOnDisabled= 223
This indicates that the JIT compilation was disabled. The JIT compilation compiles PTX. The runtime may fall back to compiling PTX if an application does not contain a suitable binary for the current device.
cudaErrorInvalidSource = 300
This indicates that the device kernel source is invalid.
cudaErrorFileNotFound = 301
This indicates that the file specified was not found.
cudaErrorSharedObjectSymbolNotFound = 302
This indicates that a link to a shared object failed to resolve.
cudaErrorSharedObjectInitFailed = 303
This indicates that initialization of a shared object failed.
cudaErrorOperatingSystem = 304
This error indicates that an OS call failed.
cudaErrorInvalidResourceHandle = 400
This indicates that a resource handle passed to the API call was not valid. Resource handles are opaque types like cudaStream_t and cudaEvent_t.
cudaErrorIllegalState = 401
This indicates that a resource required by the API call is not in a valid state to perform the requested operation.
cudaErrorSymbolNotFound = 500
This indicates that a named symbol was not found. Examples of symbols are global/constant variable names, texture names, and surface names.
cudaErrorNotReady = 600
This indicates that asynchronous operations issued previously have not completed yet. This result is not actually an error, but must be indicated differently than cudaSuccess (which indicates completion). Calls that may return this value include cudaEventQuery() and cudaStreamQuery().
cudaErrorIllegalAddress = 700
The device encountered a load or store instruction on an invalid memory address. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.
cudaErrorLaunchOutOfResources = 701
This indicates that a launch did not occur because it did not have appropriate resources. Although this error is similar to - cudaErrorInvalidConfiguration, 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.
cudaErrorLaunchTimeout = 702
This indicates that the device kernel took too long to execute. This can only occur if timeouts are enabled - see the device property kernelExecTimeoutEnabled for more information. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.
cudaErrorLaunchIncompatibleTexturing = 703
This error indicates a kernel launch that uses an incompatible texturing mode.
cudaErrorPeerAccessAlreadyEnabled = 704
This error indicates that a call to cudaDeviceEnablePeerAccess() is trying to re-enable peer addressing on from a context which has already had peer addressing enabled.
cudaErrorPeerAccessNotEnabled = 705
This error indicates that cudaDeviceDisablePeerAccess() is trying to disable peer addressing which has not been enabled yet via cudaDeviceEnablePeerAccess().
cudaErrorSetOnActiveProcess= 708
This indicates that the user has called cudaSetValidDevices(), cudaSetDeviceFlags(), cudaD3D9SetDirect3DDevice(), cudaD3D10SetDirect3DDevice, cudaD3D11SetDirect3DDevice(), or cudaVDPAUSetVDPAUDevice() after initializing the CUDA runtime by calling non-device management operations (allocating memory and launching kernels are examples of non-device management operations). This error can also be returned if using runtime/driver interoperability and there is an existing CUcontext active on the host thread.
cudaErrorCOntextIsDestroyed= 709
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.
cudaErrorAssert = 710
An assert triggered in device code during kernel execution. The device cannot be used again. All existing allocations are invalid. To continue using CUDA, the process must be terminated and relaunched.
cudaErrorTooManyPeers = 711
This error indicates that the hardware resources required to enable peer access have been exhausted for one or more of the devices passed to cudaEnablePeerAccess().
cudaErrorHostMemoryAlreadyRegistered = 712
This error indicates that the memory range passed to cudaHostRegister() has already been registered.
cudaErrorHostMemoryNotRegistered = 713
This error indicates that the pointer passed to cudaHostUnregister() does not correspond to any currently registered memory region.
cudaErrorHardwareStackError = 714
Device encountered an error in the call stack during kernel execution, possibly due to stack corruption or exceeding the stack size limit. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.
cudaErrorIllegalInstruction = 715
The device encountered an illegal instruction during kernel execution This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.
cudaErrorMisalignedAddress = 716
The device encountered a load or store instruction on a memory address which is not aligned. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.
cudaErrorInvalidAddressSpace = 717
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. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.
cudaErrorInvalidPc = 718
The device encountered an invalid program counter. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.
cudaErrorLaunchFailure = 719
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. Less common cases can be system specific - more information about these cases can be found in the system specific user guide. This leaves the process in an inconsistent state and any further CUDA work will return the same error. To continue using CUDA, the process must be terminated and relaunched.
cudaErrorCooperativeLaunchTooLarge = 720
This error indicates that the number of blocks launched per grid for a kernel that was launched via either cudaLaunchCooperativeKernel or cudaLaunchCooperativeKernelMultiDevice exceeds the maximum number of blocks as allowed by cudaOccupancyMaxActiveBlocksPerMultiprocessor or cudaOccupancyMaxActiveBlocksPerMultiprocessorWithFlags times the number of multiprocessors as specified by the device attribute cudaDevAttrMultiProcessorCount.
cudaErrorNotPermitted = 800
This error indicates the attempted operation is not permitted.
cudaErrorNotSupported = 801
This error indicates the attempted operation is not supported on the current system or device.
cudaErrorSystemNotReady = 802
This error indicates that the system is not yet ready to start any CUDA work. To continue using CUDA, verify the system configuration is in a valid state and all required driver daemons are actively running. More information about this error can be found in the system specific user guide.
cudaErrorSystemDriverMismatch = 803
This error indicates that there is a mismatch between the versions of the display driver and the CUDA driver. Refer to the compatibility documentation for supported versions.
cudaErrorCompatNotSupportedOnDevice= 804
This error indicates that the system was upgraded to run with forward compatibility but the visible hardware detected by CUDA does not support this configuration. Refer to the compatibility documentation for the supported hardware matrix or ensure that only supported hardware is visible during initialization via the CUDA_VISIBLE_DEVICES environment variable.
cudaErrorStreamCaptureUnsupported = 900
The operation is not permitted when the stream is capturing.
cudaErrorStreamCaptureInvalidated = 901
The current capture sequence on the stream has been invalidated due to a previous error.
cudaErrorStreamCaptureMerge = 902
The operation would have resulted in a merge of two independent capture sequences.
cudaErrorStreamCaptureUnmatched = 903
The capture was not initiated in this stream.
cudaErrorStreamCaptureUnjoined = 904
The capture sequence contains a fork that was not joined to the primary stream.
cudaErrorStreamCaptureIsolation = 905
A dependency would have been created which crosses the capture sequence boundary. Only implicit in-stream ordering dependencies are allowed to cross the boundary.
cudaErrorStreamCaptureImplicit = 906
The operation would have resulted in a disallowed implicit dependency on a current capture sequence from cudaStreamLegacy.
cudaErrorCapturedEvent = 907
The operation is not permitted on an event which was last recorded in a capturing stream.
cudaErrorStreamCaptureWrOngThread= 908
A stream capture sequence not initiated with the cudaStreamCaptureModeRelaxed argument to cudaStreamBeginCapture was passed to cudaStreamEndCapture in a different thread.
cudaErrorTimeout = 909
This indicates that the wait operation has timed out.
cudaErrorGraphExecUpdateFailure = 910
This error indicates that the graph update was not performed because it included changes which violated constraints specific to instantiated graph update.
cudaErrorUnknown = 999
This indicates that an unknown internal error has occurred.
cudaErrorApiFailureBase = 10000
Deprecated
This error return is deprecated as of CUDA 4.1.
Any unhandled CUDA driver error is added to this value and returned via the runtime. Production releases of CUDA should not return such errors.
https://docs.nvidia.com/cuda/cuda-runtime-api/group__CUDART__TYPES.html#group__CUDART__TYPES_1g3f51e3575c2178246db0a94a430e0038
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