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.
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.
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().
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.
This error indicates that the number of blocks launched per grid for a kernel that was
launched via either ::cuLaunchCooperativeKernel or ::cuLaunchCooperativeKernelMultiDevice
exceeds the maximum number of blocks as allowed by ::cuOccupancyMaxActiveBlocksPerMultiprocessor
or ::cuOccupancyMaxActiveBlocksPerMultiprocessorWithFlags times the number of multiprocessors
as specified by the device attribute ::CU_DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT.
This indicates that requested CUDA device is unavailable at the current
time. Devices are often unavailable due to use of
::CU_COMPUTEMODE_EXCLUSIVE_PROCESS or ::CU_COMPUTEMODE_PROHIBITED.
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.
This error indicates that the graph update was not performed because it included
changes which violated constraints specific to instantiated graph update.
While executing a kernel, the device encountered a stack error.
This can be 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.
While executing a kernel, 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.
While executing a kernel, the device encountered an illegal instruction.
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.
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.
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.
This can also be returned if the green context passed to an API call
was not converted to a ::CUcontext using ::cuCtxFromGreenCtx API.
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.
While executing a kernel, the device program counter wrapped its 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.
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.
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.
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.
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.
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.
While executing a kernel, 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.
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.
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.
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().
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.
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.
This indicates profiler is not initialized for this run. This can
happen when the application is running with external profiling tools
like visual profiler.
\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.
This error indicates that a dependency would have been created which
crosses the capture sequence boundary. Only implicit in-stream ordering
dependencies are allowed to cross the boundary.
A stream capture sequence not initiated with the ::CU_STREAM_CAPTURE_MODE_RELAXED
argument to ::cuStreamBeginCapture was passed to ::cuStreamEndCapture in a
different thread.
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.
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.
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.
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().
The API call returned with no errors. In the case of query calls, this
also means that the operation being queried is complete (see
::cuEventQuery() and ::cuStreamQuery()).
< The ::CU_STREAM_WAIT_VALUE_FLUSH flag and the ::CU_STREAM_MEM_OP_FLUSH_REMOTE_WRITES MemOp are supported on the device. See \ref CUDA_MEMOP for additional details.
< GPUDirect RDMA writes to the device do not need to be flushed for consumers within the scope indicated by the returned attribute. See ::CUGPUDirectRDMAWritesOrdering for the numerical values returned here.
< Link between the device and the host supports native atomic operations (this is a placeholder attribute, and is not supported on any current hardware)
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().
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
This error indicates that the graph update was not performed because it included
changes which violated constraints specific to instantiated graph update.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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().
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.
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.
This error indicates that ::cudaDeviceDisablePeerAccess() is trying to
disable peer addressing which has not been enabled yet via
::cudaDeviceEnablePeerAccess().
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.
This indicates profiler is not initialized for this run. This can
happen when the application is running with external profiling tools
like visual profiler.
\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.
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.
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.
A dependency would have been created which crosses the capture sequence
boundary. Only implicit in-stream ordering dependencies are allowed to
cross the boundary.
A stream capture sequence not initiated with the ::cudaStreamCaptureModeRelaxed
argument to ::cudaStreamBeginCapture was passed to ::cudaStreamEndCapture in a
different thread.
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.
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.
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.
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.
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.
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.
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.
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.
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().
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.
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()).
Flag for requesting different optimal and required granularities for an allocation.
Flag for requesting different optimal and required granularities for an allocation.