enum cudaError

cuda_error.h

enum __device_builtin__ cudaError
{
    /**
     * 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()).
     */
    cudaSuccess                           =      0,
  
    /**
     * This indicates that one or more of the parameters passed to the API call
     * is not within an acceptable range of values.
     */
    cudaErrorInvalidValue                 =     1,
  
    /**
     * The API call failed because it was unable to allocate enough memory to
     * perform the requested operation.
     */
    cudaErrorMemoryAllocation             =      2,
  
    /**
     * The API call failed because the CUDA driver and runtime could not be
     * initialized.
     */
    cudaErrorInitializationError          =      3,
  
    /**
     * 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.
     */
    cudaErrorCudartUnloading              =     4,

    /**
     * This indicates profiler is not initialized for this run. This can
     * happen when the application is running with external profiling tools
     * like visual profiler.
     */
    cudaErrorProfilerDisabled             =     5,

    /**
     * \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.
     */
    cudaErrorProfilerNotInitialized       =     6,

    /**
     * \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.
     */
    cudaErrorProfilerAlreadyStarted       =     7,

    /**
     * \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.
     */
     cudaErrorProfilerAlreadyStopped       =    8,
  
    /**
     * 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.
     */
    cudaErrorInvalidConfiguration         =      9,
  
    /**
     * This indicates that one or more of the pitch-related parameters passed
     * to the API call is not within the acceptable range for pitch.
     */
    cudaErrorInvalidPitchValue            =     12,
  
    /**
     * This indicates that the symbol name/identifier passed to the API call
     * is not a valid name or identifier.
     */
    cudaErrorInvalidSymbol                =     13,
  
    /**
     * This indicates that at least one host pointer passed to the API call is
     * not a valid host pointer.
     * \deprecated
     * This error return is deprecated as of CUDA 10.1.
     */
    cudaErrorInvalidHostPointer           =     16,
  
    /**
     * This indicates that at least one device pointer passed to the API call is
     * not a valid device pointer.
     * \deprecated
     * This error return is deprecated as of CUDA 10.1.
     */
    cudaErrorInvalidDevicePointer         =     17,
  
    /**
     * This indicates that the texture passed to the API call is not a valid
     * texture.
     */
    cudaErrorInvalidTexture               =     18,
  
    /**
     * This indicates that the texture binding is not valid. This occurs if you
     * call ::cudaGetTextureAlignmentOffset() with an unbound texture.
     */
    cudaErrorInvalidTextureBinding        =     19,
  
    /**
     * This indicates that the channel descriptor passed to the API call is not
     * valid. This occurs if the format is not one of the formats specified by
     * ::cudaChannelFormatKind, or if one of the dimensions is invalid.
     */
    cudaErrorInvalidChannelDescriptor     =     20,
  
    /**
     * This indicates that the direction of the memcpy passed to the API call is
     * not one of the types specified by ::cudaMemcpyKind.
     */
    cudaErrorInvalidMemcpyDirection       =     21,
  
    /**
     * This indicated that the user has taken the address of a constant variable,
     * which was forbidden up until the CUDA 3.1 release.
     * \deprecated
     * This error return is deprecated as of CUDA 3.1. Variables in constant
     * memory may now have their address taken by the runtime via
     * ::cudaGetSymbolAddress().
     */
    cudaErrorAddressOfConstant            =     22,
  
    /**
     * This indicated that a texture fetch was not able to be performed.
     * This was previously used for device emulation of texture operations.
     * \deprecated
     * This error return is deprecated as of CUDA 3.1. Device emulation mode was
     * removed with the CUDA 3.1 release.
     */
    cudaErrorTextureFetchFailed           =     23,
  
    /**
     * This indicated that a texture was not bound for access.
     * This was previously used for device emulation of texture operations.
     * \deprecated
     * This error return is deprecated as of CUDA 3.1. Device emulation mode was
     * removed with the CUDA 3.1 release.
     */
    cudaErrorTextureNotBound              =     24,
  
    /**
     * This indicated that a synchronization operation had failed.
     * This was previously used for some device emulation functions.
     * \deprecated
     * This error return is deprecated as of CUDA 3.1. Device emulation mode was
     * removed with the CUDA 3.1 release.
     */
    cudaErrorSynchronizationError         =     25,
  
    /**
     * This indicates that a non-float texture was being accessed with linear
     * filtering. This is not supported by CUDA.
     */
    cudaErrorInvalidFilterSetting         =     26,
  
    /**
     * This indicates that an attempt was made to read a non-float texture as a
     * normalized float. This is not supported by CUDA.
     */
    cudaErrorInvalidNormSetting           =     27,
  
    /**
     * Mixing of device and device emulation code was not allowed.
     * \deprecated
     * This error return is deprecated as of CUDA 3.1. Device emulation mode was
     * removed with the CUDA 3.1 release.
     */
    cudaErrorMixedDeviceExecution         =     28,

    /**
     * This indicates that the API call is not yet implemented. Production
     * releases of CUDA will never return this error.
     * \deprecated
     * This error return is deprecated as of CUDA 4.1.
     */
    cudaErrorNotYetImplemented            =     31,
  
    /**
     * This indicated that an emulated device pointer exceeded the 32-bit address
     * range.
     * \deprecated
     * This error return is deprecated as of CUDA 3.1. Device emulation mode was
     * removed with the CUDA 3.1 release.
     */
    cudaErrorMemoryValueTooLarge          =     32,
  
    /**
     * 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.
     */
    cudaErrorStubLibrary                  =     34,

    /**
     * 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.
     */
    cudaErrorInsufficientDriver           =     35,

    /**
     * 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.
     */
    cudaErrorCallRequiresNewerDriver      =     36,
  
    /**
     * This indicates that the surface passed to the API call is not a valid
     * surface.
     */
    cudaErrorInvalidSurface               =     37,
  
    /**
     * This indicates that multiple global or constant variables (across separate
     * CUDA source files in the application) share the same string name.
     */
    cudaErrorDuplicateVariableName        =     43,
  
    /**
     * This indicates that multiple textures (across separate CUDA source
     * files in the application) share the same string name.
     */
    cudaErrorDuplicateTextureName         =     44,
  
    /**
     * This indicates that multiple surfaces (across separate CUDA source
     * files in the application) share the same string name.
     */
    cudaErrorDuplicateSurfaceName         =     45,
  
    /**
     * This indicates that all CUDA devices are busy or unavailable at the current
     * time. Devices are often busy/unavailable due to use of
     * ::cudaComputeModeProhibited, ::cudaComputeModeExclusiveProcess, 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.
     */
    cudaErrorDevicesUnavailable           =     46,
  
    /**
     * 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 \ref CUDART_DRIVER "Interactions 
     * with the CUDA Driver API" for more information.
     */
    cudaErrorIncompatibleDriverContext    =     49,
    
    /**
     * The device function being invoked (usually via ::cudaLaunchKernel()) was not
     * previously configured via the ::cudaConfigureCall() function.
     */
    cudaErrorMissingConfiguration         =      52,
  
    /**
     * This indicated that a previous kernel launch failed. This was previously
     * used for device emulation of kernel launches.
     * \deprecated
     * This error return is deprecated as of CUDA 3.1. Device emulation mode was
     * removed with the CUDA 3.1 release.
     */
    cudaErrorPriorLaunchFailure           =      53,

    /**
     * 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. 
     */
    cudaErrorLaunchMaxDepthExceeded       =     65,

    /**
     * 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.
     */
    cudaErrorLaunchFileScopedTex          =     66,

    /**
     * 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.
     */
    cudaErrorLaunchFileScopedSurf         =     67,

    /**
     * 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. Note that ::cudaDeviceSynchronize made from device
     * runtime is only supported on devices of compute capability < 9.0.
     */
    cudaErrorSyncDepthExceeded            =     68,

    /**
     * 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.
     */
    cudaErrorLaunchPendingCountExceeded   =     69,
  
    /**
     * The requested device function does not exist or is not compiled for the
     * proper device architecture.
     */
    cudaErrorInvalidDeviceFunction        =      98,
  
    /**
     * This indicates that no CUDA-capable devices were detected by the installed
     * CUDA driver.
     */
    cudaErrorNoDevice                     =     100,
  
    /**
     * This indicates that the device ordinal supplied by the user does not
     * correspond to a valid CUDA device or that the action requested is
     * invalid for the specified device.
     */
    cudaErrorInvalidDevice                =     101,

    /**
     * This indicates that the device doesn't have a valid Grid License.
     */
    cudaErrorDeviceNotLicensed            =     102,

   /**
    * 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.
    */
   cudaErrorSoftwareValidityNotEstablished  =     103,

    /**
     * This indicates an internal startup failure in the CUDA runtime.
     */
    cudaErrorStartupFailure               =    127,
  
    /**
     * This indicates that the device kernel image is invalid.
     */
    cudaErrorInvalidKernelImage           =     200,

    /**
     * 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.
     */
    cudaErrorDeviceUninitialized          =     201,

    /**
     * This indicates that the buffer object could not be mapped.
     */
    cudaErrorMapBufferObjectFailed        =     205,
  
    /**
     * This indicates that the buffer object could not be unmapped.
     */
    cudaErrorUnmapBufferObjectFailed      =     206,

    /**
     * This indicates that the specified array is currently mapped and thus
     * cannot be destroyed.
     */
    cudaErrorArrayIsMapped                =     207,

    /**
     * This indicates that the resource is already mapped.
     */
    cudaErrorAlreadyMapped                =     208,
  
    /**
     * 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.
     */
    cudaErrorNoKernelImageForDevice       =     209,

    /**
     * This indicates that a resource has already been acquired.
     */
    cudaErrorAlreadyAcquired              =     210,

    /**
     * This indicates that a resource is not mapped.
     */
    cudaErrorNotMapped                    =     211,

    /**
     * This indicates that a mapped resource is not available for access as an
     * array.
     */
    cudaErrorNotMappedAsArray             =     212,

    /**
     * This indicates that a mapped resource is not available for access as a
     * pointer.
     */
    cudaErrorNotMappedAsPointer           =     213,
  
    /**
     * This indicates that an uncorrectable ECC error was detected during
     * execution.
     */
    cudaErrorECCUncorrectable             =     214,
  
    /**
     * This indicates that the ::cudaLimit passed to the API call is not
     * supported by the active device.
     */
    cudaErrorUnsupportedLimit             =     215,
    
    /**
     * This indicates that a call tried to access an exclusive-thread device that 
     * is already in use by a different thread.
     */
    cudaErrorDeviceAlreadyInUse           =     216,

    /**
     * This error indicates that P2P access is not supported across the given
     * devices.
     */
    cudaErrorPeerAccessUnsupported        =     217,

    /**
     * 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.
     */
    cudaErrorInvalidPtx                   =     218,

    /**
     * This indicates an error with the OpenGL or DirectX context.
     */
    cudaErrorInvalidGraphicsContext       =     219,

    /**
     * This indicates that an uncorrectable NVLink error was detected during the
     * execution.
     */
    cudaErrorNvlinkUncorrectable          =     220,

    /**
     * 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.
     */
    cudaErrorJitCompilerNotFound          =     221,

    /**
     * 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.
     */
    cudaErrorUnsupportedPtxVersion        =     222,

    /**
     * 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.
     */
    cudaErrorJitCompilationDisabled       =     223,

    /**
     * This indicates that the provided execution affinity is not supported by the device.
     */
    cudaErrorUnsupportedExecAffinity      =     224,

    /**
     * This indicates that the code to be compiled by the PTX JIT contains
     * unsupported call to cudaDeviceSynchronize.
     */
    cudaErrorUnsupportedDevSideSync       =     225,

    /**
     * This indicates that the device kernel source is invalid.
     */
    cudaErrorInvalidSource                =     300,

    /**
     * This indicates that the file specified was not found.
     */
    cudaErrorFileNotFound                 =     301,
  
    /**
     * This indicates that a link to a shared object failed to resolve.
     */
    cudaErrorSharedObjectSymbolNotFound   =     302,
  
    /**
     * This indicates that initialization of a shared object failed.
     */
    cudaErrorSharedObjectInitFailed       =     303,

    /**
     * This error indicates that an OS call failed.
     */
    cudaErrorOperatingSystem              =     304,
  
    /**
     * 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.
     */
    cudaErrorInvalidResourceHandle        =     400,

    /**
     * This indicates that a resource required by the API call is not in a
     * valid state to perform the requested operation.
     */
    cudaErrorIllegalState                 =     401,

    /**
     * This indicates that a named symbol was not found. Examples of symbols
     * are global/constant variable names, driver function names, texture names,
     * and surface names.
     */
    cudaErrorSymbolNotFound               =     500,
  
    /**
     * 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().
     */
    cudaErrorNotReady                     =     600,

    /**
     * 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.
     */
    cudaErrorIllegalAddress               =     700,
  
    /**
     * 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.
     */
    cudaErrorLaunchOutOfResources         =      701,
  
    /**
     * This indicates that the device kernel took too long to execute. This can
     * only occur if timeouts are enabled - see the device property
     * \ref ::cudaDeviceProp::kernelExecTimeoutEnabled "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.
     */
    cudaErrorLaunchTimeout                =      702,

    /**
     * This error indicates a kernel launch that uses an incompatible texturing
     * mode.
     */
    cudaErrorLaunchIncompatibleTexturing  =     703,
      
    /**
     * 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.
     */
    cudaErrorPeerAccessAlreadyEnabled     =     704,
    
    /**
     * This error indicates that ::cudaDeviceDisablePeerAccess() is trying to 
     * disable peer addressing which has not been enabled yet via 
     * ::cudaDeviceEnablePeerAccess().
     */
    cudaErrorPeerAccessNotEnabled         =     705,
  
    /**
     * 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.
     */
    cudaErrorSetOnActiveProcess           =     708,

    /**
     * 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.
     */
    cudaErrorContextIsDestroyed           =     709,

    /**
     * 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.
     */
    cudaErrorAssert                        =    710,
  
    /**
     * 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().
     */
    cudaErrorTooManyPeers                 =     711,
  
    /**
     * This error indicates that the memory range passed to ::cudaHostRegister()
     * has already been registered.
     */
    cudaErrorHostMemoryAlreadyRegistered  =     712,
        
    /**
     * This error indicates that the pointer passed to ::cudaHostUnregister()
     * does not correspond to any currently registered memory region.
     */
    cudaErrorHostMemoryNotRegistered      =     713,

    /**
     * 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.
     */
    cudaErrorHardwareStackError           =     714,

    /**
     * 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.
     */
    cudaErrorIllegalInstruction           =     715,

    /**
     * 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.
     */
    cudaErrorMisalignedAddress            =     716,

    /**
     * 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.
     */
    cudaErrorInvalidAddressSpace          =     717,

    /**
     * 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.
     */
    cudaErrorInvalidPc                    =     718,
  
    /**
     * 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.
     */
    cudaErrorLaunchFailure                =      719,

    /**
     * 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.
     */
    cudaErrorCooperativeLaunchTooLarge    =     720,
    
    /**
     * This error indicates the attempted operation is not permitted.
     */
    cudaErrorNotPermitted                 =     800,

    /**
     * This error indicates the attempted operation is not supported
     * on the current system or device.
     */
    cudaErrorNotSupported                 =     801,

    /**
     * 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.
     */
    cudaErrorSystemNotReady               =     802,

    /**
     * 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.
     */
    cudaErrorSystemDriverMismatch         =     803,

    /**
     * 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.
     */
    cudaErrorCompatNotSupportedOnDevice   =     804,

    /**
     * This error indicates that the MPS client failed to connect to the MPS control daemon or the MPS server.
     */
    cudaErrorMpsConnectionFailed          =     805,

    /**
     * This error indicates that the remote procedural call between the MPS server and the MPS client failed.
     */
    cudaErrorMpsRpcFailure                =     806,

    /**
     * This error indicates that the MPS server is not ready to accept new MPS client requests.
     * This error can be returned when the MPS server is in the process of recovering from a fatal failure.
     */
    cudaErrorMpsServerNotReady            =     807,

    /**
     * This error indicates that the hardware resources required to create MPS client have been exhausted.
     */
    cudaErrorMpsMaxClientsReached         =     808,

    /**
     * This error indicates the the hardware resources required to device connections have been exhausted.
     */
    cudaErrorMpsMaxConnectionsReached     =     809,

    /**
     * This error indicates that the MPS client has been terminated by the server. To continue using CUDA, the process must be terminated and relaunched.
     */
    cudaErrorMpsClientTerminated          =     810,

    /**
     * This error indicates, that the program is using CUDA Dynamic Parallelism, but the current configuration, like MPS, does not support it.
     */
    cudaErrorCdpNotSupported              =     811,

    /**
     * This error indicates, that the program contains an unsupported interaction between different versions of CUDA Dynamic Parallelism.
     */
    cudaErrorCdpVersionMismatch           =     812,

    /**
     * The operation is not permitted when the stream is capturing.
     */
    cudaErrorStreamCaptureUnsupported     =    900,

    /**
     * The current capture sequence on the stream has been invalidated due to
     * a previous error.
     */
    cudaErrorStreamCaptureInvalidated     =    901,

    /**
     * The operation would have resulted in a merge of two independent capture
     * sequences.
     */
    cudaErrorStreamCaptureMerge           =    902,

    /**
     * The capture was not initiated in this stream.
     */
    cudaErrorStreamCaptureUnmatched       =    903,

    /**
     * The capture sequence contains a fork that was not joined to the primary
     * stream.
     */
    cudaErrorStreamCaptureUnjoined        =    904,

    /**
     * A dependency would have been created which crosses the capture sequence
     * boundary. Only implicit in-stream ordering dependencies are allowed to
     * cross the boundary.
     */
    cudaErrorStreamCaptureIsolation       =    905,

    /**
     * The operation would have resulted in a disallowed implicit dependency on
     * a current capture sequence from cudaStreamLegacy.
     */
    cudaErrorStreamCaptureImplicit        =    906,

    /**
     * The operation is not permitted on an event which was last recorded in a
     * capturing stream.
     */
    cudaErrorCapturedEvent                =    907,
  
    /**
     * A stream capture sequence not initiated with the ::cudaStreamCaptureModeRelaxed
     * argument to ::cudaStreamBeginCapture was passed to ::cudaStreamEndCapture in a
     * different thread.
     */
    cudaErrorStreamCaptureWrongThread     =    908,

    /**
     * This indicates that the wait operation has timed out.
     */
    cudaErrorTimeout                      =    909,

    /**
     * This error indicates that the graph update was not performed because it included 
     * changes which violated constraints specific to instantiated graph update.
     */
    cudaErrorGraphExecUpdateFailure       =    910,

    /**
     * This indicates that an async error has occurred in a device outside of CUDA.
     * If CUDA was waiting for an external device's signal before consuming shared data,
     * the external device signaled an error indicating that the data is not valid for
     * consumption. 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.
     */
    cudaErrorExternalDevice               =    911,

    /**
     * This indicates that a kernel launch error has occurred due to cluster
     * misconfiguration.
     */
    cudaErrorInvalidClusterSize           =    912,

    /**
     * This indicates that an unknown internal error has occurred.
     */
    cudaErrorUnknown                      =    999,

    /**
     * Any unhandled CUDA driver error is added to this value and returned via
     * the runtime. Production releases of CUDA should not return such errors.
     * \deprecated
     * This error return is deprecated as of CUDA 4.1.
     */
    cudaErrorApiFailureBase               =  10000
};