Struct cudaError_t 

#[repr(transparent)]
pub struct cudaError_t(pub c_uint);

CUDA Error types CUDA error types

Tuple Fields

0: c_uint

Implementations

impl cudaError

pub const cudaSuccess: 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()).

pub const cudaErrorInvalidValue: cudaError

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

pub const cudaErrorMemoryAllocation: cudaError

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

pub const cudaErrorInitializationError: cudaError

The API call failed because the CUDA driver and runtime could not be initialized.

pub const cudaErrorCudartUnloading: cudaError

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.

pub const cudaErrorProfilerDisabled: cudaError

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

pub const cudaErrorProfilerNotInitialized: cudaError

\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.

pub const cudaErrorProfilerAlreadyStarted: cudaError

\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.

pub const cudaErrorProfilerAlreadyStopped: cudaError

\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.

pub const cudaErrorInvalidConfiguration: cudaError

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.

pub const cudaErrorVersionTranslation: cudaError

This indicates that the driver is newer than the runtime version and returned graph node parameter information that the runtime does not understand and is unable to translate.

pub const cudaErrorInvalidPitchValue: cudaError

This indicates that one or more of the pitch-related parameters passed to the API call is not within the acceptable range for pitch.

pub const cudaErrorInvalidSymbol: cudaError

This indicates that the symbol name/identifier passed to the API call is not a valid name or identifier.

pub const cudaErrorInvalidHostPointer: cudaError

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.

pub const cudaErrorInvalidDevicePointer: cudaError

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.

pub const cudaErrorInvalidTexture: cudaError

This indicates that the texture passed to the API call is not a valid texture.

pub const cudaErrorInvalidTextureBinding: cudaError

This indicates that the texture binding is not valid. This occurs if you call ::cudaGetTextureAlignmentOffset() with an unbound texture.

pub const cudaErrorInvalidChannelDescriptor: cudaError

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.

pub const cudaErrorInvalidMemcpyDirection: cudaError

This indicates that the direction of the memcpy passed to the API call is not one of the types specified by ::cudaMemcpyKind.

pub const cudaErrorAddressOfConstant: cudaError

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().

pub const cudaErrorTextureFetchFailed: cudaError

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.

pub const cudaErrorTextureNotBound: cudaError

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.

pub const cudaErrorSynchronizationError: cudaError

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.

pub const cudaErrorInvalidFilterSetting: cudaError

This indicates that a non-float texture was being accessed with linear filtering. This is not supported by CUDA.

pub const cudaErrorInvalidNormSetting: cudaError

This indicates that an attempt was made to read an unsupported data type as a normalized float. This is not supported by CUDA.

pub const cudaErrorMixedDeviceExecution: cudaError

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.

pub const cudaErrorNotYetImplemented: cudaError

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.

pub const cudaErrorMemoryValueTooLarge: cudaError

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.

pub const cudaErrorStubLibrary: cudaError

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.

pub const cudaErrorInsufficientDriver: cudaError

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.

pub const cudaErrorCallRequiresNewerDriver: cudaError

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.

pub const cudaErrorInvalidSurface: cudaError

This indicates that the surface passed to the API call is not a valid surface.

pub const cudaErrorDuplicateVariableName: cudaError

This indicates that multiple global or constant variables (across separate CUDA source files in the application) share the same string name.

pub const cudaErrorDuplicateTextureName: cudaError

This indicates that multiple textures (across separate CUDA source files in the application) share the same string name.

pub const cudaErrorDuplicateSurfaceName: cudaError

This indicates that multiple surfaces (across separate CUDA source files in the application) share the same string name.

pub const cudaErrorDevicesUnavailable: cudaError

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.

pub const cudaErrorIncompatibleDriverContext: cudaError

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.

pub const cudaErrorMissingConfiguration: cudaError

The device function being invoked (usually via ::cudaLaunchKernel()) was not previously configured via the ::cudaConfigureCall() function.

pub const cudaErrorPriorLaunchFailure: cudaError

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.

pub const cudaErrorLaunchMaxDepthExceeded: cudaError

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.

pub const cudaErrorLaunchFileScopedTex: cudaError

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.

pub const cudaErrorLaunchFileScopedSurf: cudaError

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.

pub const cudaErrorSyncDepthExceeded: cudaError

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.

pub const cudaErrorLaunchPendingCountExceeded: cudaError

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.

pub const cudaErrorInvalidDeviceFunction: cudaError

The requested device function does not exist or is not compiled for the proper device architecture.

pub const cudaErrorNoDevice: cudaError

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

pub const cudaErrorInvalidDevice: cudaError

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.

pub const cudaErrorDeviceNotLicensed: cudaError

This indicates that the device doesn’t have a valid Grid License.

pub const cudaErrorSoftwareValidityNotEstablished: cudaError

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.

pub const cudaErrorStartupFailure: cudaError

This indicates an internal startup failure in the CUDA runtime.

pub const cudaErrorInvalidKernelImage: cudaError

This indicates that the device kernel image is invalid.

pub const cudaErrorDeviceUninitialized: cudaError

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.

pub const cudaErrorMapBufferObjectFailed: cudaError

This indicates that the buffer object could not be mapped.

pub const cudaErrorUnmapBufferObjectFailed: cudaError

This indicates that the buffer object could not be unmapped.

pub const cudaErrorArrayIsMapped: cudaError

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

pub const cudaErrorAlreadyMapped: cudaError

This indicates that the resource is already mapped.

pub const cudaErrorNoKernelImageForDevice: cudaError

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.

pub const cudaErrorAlreadyAcquired: cudaError

This indicates that a resource has already been acquired.

pub const cudaErrorNotMapped: cudaError

This indicates that a resource is not mapped.

pub const cudaErrorNotMappedAsArray: cudaError

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

pub const cudaErrorNotMappedAsPointer: cudaError

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

pub const cudaErrorECCUncorrectable: cudaError

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

pub const cudaErrorUnsupportedLimit: cudaError

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

pub const cudaErrorDeviceAlreadyInUse: cudaError

This indicates that a call tried to access an exclusive-thread device that is already in use by a different thread.

pub const cudaErrorPeerAccessUnsupported: cudaError

This error indicates that P2P access is not supported across the given devices.

pub const cudaErrorInvalidPtx: cudaError

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.

pub const cudaErrorInvalidGraphicsContext: cudaError

This indicates an error with the OpenGL or DirectX context.

pub const cudaErrorNvlinkUncorrectable: cudaError

This indicates that an uncorrectable NVLink error was detected during the execution.

pub const cudaErrorJitCompilerNotFound: cudaError

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.

pub const cudaErrorUnsupportedPtxVersion: cudaError

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.

pub const cudaErrorJitCompilationDisabled: cudaError

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.

pub const cudaErrorUnsupportedExecAffinity: cudaError

This indicates that the provided execution affinity is not supported by the device.

pub const cudaErrorUnsupportedDevSideSync: cudaError

This indicates that the code to be compiled by the PTX JIT contains unsupported call to cudaDeviceSynchronize.

pub const cudaErrorContained: cudaError

This indicates that an exception occurred on the device that is now contained by the GPU’s error containment capability. Common causes are - a. Certain types of invalid accesses of peer GPU memory over nvlink b. Certain classes of hardware errors 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.

pub const cudaErrorInvalidSource: cudaError

This indicates that the device kernel source is invalid.

pub const cudaErrorFileNotFound: cudaError

This indicates that the file specified was not found.

pub const cudaErrorSharedObjectSymbolNotFound: cudaError

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

pub const cudaErrorSharedObjectInitFailed: cudaError

This indicates that initialization of a shared object failed.

pub const cudaErrorOperatingSystem: cudaError

This error indicates that an OS call failed.

pub const cudaErrorInvalidResourceHandle: cudaError

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.

pub const cudaErrorIllegalState: cudaError

This indicates that a resource required by the API call is not in a valid state to perform the requested operation.

pub const cudaErrorLossyQuery: cudaError

This indicates an attempt was made to introspect an object in a way that would discard semantically important information. This is either due to the object using funtionality newer than the API version used to introspect it or omission of optional return arguments.

pub const cudaErrorSymbolNotFound: cudaError

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.

pub const cudaErrorNotReady: cudaError

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().

pub const cudaErrorIllegalAddress: cudaError

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.

pub const cudaErrorLaunchOutOfResources: cudaError

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.

pub const cudaErrorLaunchTimeout: cudaError

This indicates that the device kernel took too long to execute. This can only occur if timeouts are enabled - see the device attribute \ref ::cudaDeviceAttr::cudaDevAttrKernelExecTimeout “cudaDevAttrKernelExecTimeout” 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.

pub const cudaErrorLaunchIncompatibleTexturing: cudaError

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

pub const cudaErrorPeerAccessAlreadyEnabled: cudaError

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.

pub const cudaErrorPeerAccessNotEnabled: cudaError

This error indicates that ::cudaDeviceDisablePeerAccess() is trying to disable peer addressing which has not been enabled yet via ::cudaDeviceEnablePeerAccess().

pub const cudaErrorSetOnActiveProcess: cudaError

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.

pub const cudaErrorContextIsDestroyed: cudaError

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.

pub const cudaErrorAssert: cudaError

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.

pub const cudaErrorTooManyPeers: cudaError

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().

pub const cudaErrorHostMemoryAlreadyRegistered: cudaError

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

pub const cudaErrorHostMemoryNotRegistered: cudaError

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

pub const cudaErrorHardwareStackError: cudaError

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.

pub const cudaErrorIllegalInstruction: cudaError

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.

pub const cudaErrorMisalignedAddress: cudaError

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.

pub const cudaErrorInvalidAddressSpace: cudaError

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.

pub const cudaErrorInvalidPc: cudaError

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.

pub const cudaErrorLaunchFailure: cudaError

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.

pub const cudaErrorCooperativeLaunchTooLarge: cudaError

This error indicates that the number of blocks launched per grid for a kernel that was launched via either ::cudaLaunchCooperativeKernel exceeds the maximum number of blocks as allowed by ::cudaOccupancyMaxActiveBlocksPerMultiprocessor or ::cudaOccupancyMaxActiveBlocksPerMultiprocessorWithFlags times the number of multiprocessors as specified by the device attribute ::cudaDevAttrMultiProcessorCount.

pub const cudaErrorTensorMemoryLeak: cudaError

An exception occurred on the device while exiting a kernel using tensor memory: the tensor memory was not completely deallocated. 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.

pub const cudaErrorNotPermitted: cudaError

This error indicates the attempted operation is not permitted.

pub const cudaErrorNotSupported: cudaError

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

pub const cudaErrorSystemNotReady: cudaError

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.

pub const cudaErrorSystemDriverMismatch: cudaError

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.

pub const cudaErrorCompatNotSupportedOnDevice: cudaError

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.

pub const cudaErrorMpsConnectionFailed: cudaError

This error indicates that the MPS client failed to connect to the MPS control daemon or the MPS server.

pub const cudaErrorMpsRpcFailure: cudaError

This error indicates that the remote procedural call between the MPS server and the MPS client failed.

pub const cudaErrorMpsServerNotReady: cudaError

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.

pub const cudaErrorMpsMaxClientsReached: cudaError

This error indicates that the hardware resources required to create MPS client have been exhausted.

pub const cudaErrorMpsMaxConnectionsReached: cudaError

This error indicates the the hardware resources required to device connections have been exhausted.

pub const cudaErrorMpsClientTerminated: cudaError

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

pub const cudaErrorCdpNotSupported: cudaError

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

pub const cudaErrorCdpVersionMismatch: cudaError

This error indicates, that the program contains an unsupported interaction between different versions of CUDA Dynamic Parallelism.

pub const cudaErrorStreamCaptureUnsupported: cudaError

The operation is not permitted when the stream is capturing.

pub const cudaErrorStreamCaptureInvalidated: cudaError

The current capture sequence on the stream has been invalidated due to a previous error.

pub const cudaErrorStreamCaptureMerge: cudaError

The operation would have resulted in a merge of two independent capture sequences.

pub const cudaErrorStreamCaptureUnmatched: cudaError

The capture was not initiated in this stream.

pub const cudaErrorStreamCaptureUnjoined: cudaError

The capture sequence contains a fork that was not joined to the primary stream.

pub const cudaErrorStreamCaptureIsolation: cudaError

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

pub const cudaErrorStreamCaptureImplicit: cudaError

The operation would have resulted in a disallowed implicit dependency on a current capture sequence from cudaStreamLegacy.

pub const cudaErrorCapturedEvent: cudaError

The operation is not permitted on an event which was last recorded in a capturing stream.

pub const cudaErrorStreamCaptureWrongThread: cudaError

A stream capture sequence not initiated with the ::cudaStreamCaptureModeRelaxed argument to ::cudaStreamBeginCapture was passed to ::cudaStreamEndCapture in a different thread.

pub const cudaErrorTimeout: cudaError

This indicates that the wait operation has timed out.

pub const cudaErrorGraphExecUpdateFailure: cudaError

This error indicates that the graph update was not performed because it included changes which violated constraints specific to instantiated graph update.

pub const cudaErrorExternalDevice: cudaError

This indicates that an error has occurred in a device outside of GPU. It can be a synchronous error w.r.t. CUDA API or an asynchronous error from the external device. In case of asynchronous error, it means that 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. In case of synchronous error, it means that one or more external devices have encountered an error and cannot complete the operation.

pub const cudaErrorInvalidClusterSize: cudaError

This indicates that a kernel launch error has occurred due to cluster misconfiguration.

pub const cudaErrorFunctionNotLoaded: cudaError

Indiciates a function handle is not loaded when calling an API that requires a loaded function.

pub const cudaErrorInvalidResourceType: cudaError

This error indicates one or more resources passed in are not valid resource types for the operation.

pub const cudaErrorInvalidResourceConfiguration: cudaError

This error indicates one or more resources are insufficient or non-applicable for the operation.

pub const cudaErrorStreamDetached: cudaError

This error indicates that the requested operation is not permitted because the stream is in a detached state. This can occur if the green context associated with the stream has been destroyed, limiting the stream’s operational capabilities.

pub const cudaErrorUnknown: cudaError

This indicates that an unknown internal error has occurred.

pub const cudaErrorApiFailureBase: cudaError

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.

Trait Implementations

impl Clone for cudaError

fn clone(&self) -> cudaError

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for cudaError

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl Hash for cudaError

fn hash<__H: Hasher>(&self, state: &mut __H)

Feeds this value into the given Hasher.

fn hash_slice<H>(data: &[Self], state: &mut H)

where H: Hasher, Self: Sized,

Feeds a slice of this type into the given Hasher.

impl PartialEq for cudaError

fn eq(&self, other: &cudaError) -> bool

Tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &Rhs) -> bool

Tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl Copy for cudaError

impl Eq for cudaError

impl StructuralPartialEq for cudaError