Select compute-device which best matches criteria.
Blocks until remote writes are visible to the specified scope.
Returns information about the device.
Returns a handle to a compute device.
Returns the preferred cache configuration for the current device.
Returns the default mempool of a device.
Queries details about atomic operations supported between the device and host.
Returns in *capabilities the details about requested atomic *operations over the the link between dev and the host. The allocated size of *operations and *capabilities must be count.
For each cudaAtomicOperation in *operations, the corresponding result in *capabilities will be a bitmask indicating which of cudaAtomicOperationCapability the link supports natively.
Returns cudaErrorInvalidDevice if dev is not valid.
Returns cudaErrorInvalidValue if *capabilities or *operations is NULL, if count is 0, or if any of *operations is not valid.
Note:Note that this function may also return error codes from previous, asynchronous launches.
See also:
cudaDeviceGetAttribute, cudaDeviceGetP2PAtomicCapabilities, cuDeviceGeHostAtomicCapabilities
Return resource limits.
Gets the current mempool for a device.
Return NvSciSync attributes that this device can support.
Returns in nvSciSyncAttrList, the properties of NvSciSync that this CUDA device, dev can support. The returned nvSciSyncAttrList can be used to create an NvSciSync that matches this device's capabilities.
If NvSciSyncAttrKey_RequiredPerm field in nvSciSyncAttrList is already set this API will return cudaErrorInvalidValue.
The applications should set nvSciSyncAttrList to a valid NvSciSyncAttrList failing which this API will return cudaErrorInvalidHandle.
The flags controls how applications intends to use the NvSciSync created from the nvSciSyncAttrList. The valid flags are:
cudaNvSciSyncAttrSignal, specifies that the applications intends to signal an NvSciSync on this CUDA device.
cudaNvSciSyncAttrWait, specifies that the applications intends to wait on an NvSciSync on this CUDA device.
At least one of these flags must be set, failing which the API returns cudaErrorInvalidValue. Both the flags are orthogonal to one another: a developer may set both these flags that allows to set both wait and signal specific attributes in the same nvSciSyncAttrList.
Note that this API updates the input nvSciSyncAttrList with values equivalent to the following public attribute key-values: NvSciSyncAttrKey_RequiredPerm is set to
NvSciSyncAccessPerm_SignalOnly if cudaNvSciSyncAttrSignal is set in flags.
NvSciSyncAccessPerm_WaitOnly if cudaNvSciSyncAttrWait is set in flags.
NvSciSyncAccessPerm_WaitSignal if both cudaNvSciSyncAttrWait and cudaNvSciSyncAttrSignal are set in flags. NvSciSyncAttrKey_PrimitiveInfo is set to
NvSciSyncAttrValPrimitiveType_SysmemSemaphore on any valid device.
NvSciSyncAttrValPrimitiveType_Syncpoint if device is a Tegra device.
NvSciSyncAttrValPrimitiveType_SysmemSemaphorePayload64b if device is GA10X+. NvSciSyncAttrKey_GpuId is set to the same UUID that is returned in cudaDeviceProp.uuid from cudaDeviceGetProperties for this device.
cudaSuccess, cudaErrorDeviceUninitialized, cudaErrorInvalidValue, cudaErrorInvalidHandle, cudaErrorInvalidDevice, cudaErrorNotSupported, cudaErrorMemoryAllocation
See also:
cudaImportExternalSemaphore, cudaDestroyExternalSemaphore, cudaSignalExternalSemaphoresAsync, cudaWaitExternalSemaphoresAsync
Queries details about atomic operations supported between two devices.
Queries attributes of the link between two devices.
Returns a PCI Bus Id string for the device.
Returns an ASCII string identifying the device dev in the NULL-terminated string pointed to by pciBusId. len specifies the maximum length of the string that may be returned.
See also:
Returns numerical values that correspond to the least and greatest stream priorities.
Returns in *leastPriority and *greatestPriority the numerical values that correspond to the least and greatest stream priorities respectively. Stream priorities follow a convention where lower numbers imply greater priorities. The range of meaningful stream priorities is given by [*greatestPriority, *leastPriority]. If the user attempts to create a stream with a priority value that is outside the the meaningful range as specified by this API, the priority is automatically clamped down or up to either *leastPriority or *greatestPriority respectively. See cudaStreamCreateWithPriority for details on creating a priority stream. A NULL may be passed in for *leastPriority or *greatestPriority if the value is not desired.
This function will return '0' in both *leastPriority and *greatestPriority if the current context's device does not support stream priorities (see cudaDeviceGetAttribute).
See also:
cudaStreamCreateWithPriority, cudaStreamGetPriority, cuCtxGetStreamPriorityRange
Returns the maximum number of elements allocatable in a 1D linear texture for a given element size.
Returns in maxWidthInElements the maximum number of elements allocatable in a 1D linear texture for given format descriptor fmtDesc.
See also:
Registers a callback function to receive async notifications.
Registers callbackFunc to receive async notifications.
The userData parameter is passed to the callback function at async notification time. Likewise, callback is also passed to the callback function to distinguish between multiple registered callbacks.
The callback function being registered should be designed to return quickly (~10ms). Any long running tasks should be queued for execution on an application thread.
Callbacks may not call cudaDeviceRegisterAsyncNotification or cudaDeviceUnregisterAsyncNotification. Doing so will result in cudaErrorNotPermitted. Async notification callbacks execute in an undefined order and may be serialized.
Returns in *callback a handle representing the registered callback instance.
Note:Note that this function may also return error codes from previous, asynchronous launches.
See also:
Destroy all allocations and reset all state on the current device in the current process.
Explicitly destroys and cleans up all resources associated with the current device in the current process. It is the caller's responsibility to ensure that the resources are not accessed or passed in subsequent API calls and doing so will result in undefined behavior. These resources include CUDA types cudaStream_t, cudaEvent_t, cudaArray_t, cudaMipmappedArray_t, cudaPitchedPtr, cudaTextureObject_t, cudaSurfaceObject_t, textureReference, surfaceReference, cudaExternalMemory_t, cudaExternalSemaphore_t and cudaGraphicsResource_t. These resources also include memory allocations by cudaMalloc, cudaMallocHost, cudaMallocManaged and cudaMallocPitch. Any subsequent API call to this device will reinitialize the device.
Note that this function will reset the device immediately. It is the caller's responsibility to ensure that the device is not being accessed by any other host threads from the process when this function is called.
See also:
Sets the preferred cache configuration for the current device.
Set resource limits.
Setting limit to value is a request by the application to update the current limit maintained by the device. The driver is free to modify the requested value to meet h/w requirements (this could be clamping to minimum or maximum values, rounding up to nearest element size, etc). The application can use cudaDeviceGetLimit() to find out exactly what the limit has been set to.
Setting each cudaLimit has its own specific restrictions, so each is discussed here.
cudaLimitStackSize controls the stack size in bytes of each GPU thread.
cudaLimitPrintfFifoSize controls the size in bytes of the shared FIFO used by the printf() device system call. Setting cudaLimitPrintfFifoSize must not be performed after launching any kernel that uses the printf() device system call - in such case cudaErrorInvalidValue will be returned.
cudaLimitMallocHeapSize controls the size in bytes of the heap used by the malloc() and free() device system calls. Setting cudaLimitMallocHeapSize must not be performed after launching any kernel that uses the malloc() or free() device system calls - in such case cudaErrorInvalidValue will be returned.
cudaLimitDevRuntimeSyncDepth controls the maximum nesting depth of a grid at which a thread can safely call cudaDeviceSynchronize(). Setting this limit must be performed before any launch of a kernel that uses the device runtime and calls cudaDeviceSynchronize() above the default sync depth, two levels of grids. Calls to cudaDeviceSynchronize() will fail with error code cudaErrorSyncDepthExceeded if the limitation is violated. This limit can be set smaller than the default or up the maximum launch depth of 24. When setting this limit, keep in mind that additional levels of sync depth require the runtime to reserve large amounts of device memory which can no longer be used for user allocations. If these reservations of device memory fail, cudaDeviceSetLimit will return cudaErrorMemoryAllocation, and the limit can be reset to a lower value. This limit is only applicable to devices of compute capability < 9.0. Attempting to set this limit on devices of other compute capability will results in error cudaErrorUnsupportedLimit being returned.
cudaLimitDevRuntimePendingLaunchCount controls the maximum number of outstanding device runtime launches that can be made from the current device. A grid is outstanding from the point of launch up until the grid is known to have been completed. Device runtime launches which violate this limitation fail and return cudaErrorLaunchPendingCountExceeded when cudaGetLastError() is called after launch. If more pending launches than the default (2048 launches) are needed for a module using the device runtime, this limit can be increased. Keep in mind that being able to sustain additional pending launches will require the runtime to reserve larger amounts of device memory upfront which can no longer be used for allocations. If these reservations fail, cudaDeviceSetLimit will return cudaErrorMemoryAllocation, and the limit can be reset to a lower value. This limit is only applicable to devices of compute capability 3.5 and higher. Attempting to set this limit on devices of compute capability less than 3.5 will result in the error cudaErrorUnsupportedLimit being returned.
cudaLimitMaxL2FetchGranularity controls the L2 cache fetch granularity. Values can range from 0B to 128B. This is purely a performance hint and it can be ignored or clamped depending on the platform.
cudaLimitPersistingL2CacheSize controls size in bytes available for persisting L2 cache. This is purely a performance hint and it can be ignored or clamped depending on the platform.
See also:
Sets the current memory pool of a device.
The memory pool must be local to the specified device. Unless a mempool is specified in the cudaMallocAsync call, cudaMallocAsync allocates from the current mempool of the provided stream's device. By default, a device's current memory pool is its default memory pool.
Note:Use cudaMallocFromPoolAsync to specify asynchronous allocations from a device different than the one the stream runs on.
Note:Note that this function may also return error codes from previous, asynchronous launches.
Note that as specified by cudaStreamAddCallback no CUDA function may be called from callback. cudaErrorNotPermitted may, but is not guaranteed to, be returned as a diagnostic in such case.
See also:
cuDeviceSetMemPool, cudaDeviceGetMemPool, cudaDeviceGetDefaultMemPool, cudaMemPoolCreate, cudaMemPoolDestroy, cudaMallocFromPoolAsync
Wait for compute device to finish.
Blocks until the device has completed all preceding requested tasks. cudaDeviceSynchronize() returns an error if one of the preceding tasks has failed. If the cudaDeviceScheduleBlockingSync flag was set for this device, the host thread will block until the device has finished its work.
Note:Use of cudaDeviceSynchronize in device code was deprecated in CUDA 11.6 and removed for compute_90+ compilation. For compute capability < 9.0, compile-time opt-in by specifying -D CUDA_FORCE_CDP1_IF_SUPPORTED is required to continue using cudaDeviceSynchronize() in device code for now. Note that this is different from host-side cudaDeviceSynchronize, which is still supported.
Note that this function may also return error codes from previous, asynchronous launches.
Note that this function may also return cudaErrorInitializationError, cudaErrorInsufficientDriver or cudaErrorNoDevice if this call tries to initialize internal CUDA RT state.
Note that as specified by cudaStreamAddCallback no CUDA function may be called from callback. cudaErrorNotPermitted may, but is not guaranteed to, be returned as a diagnostic in such case.
See also:
Unregisters an async notification callback.
Unregisters callback so that the corresponding callback function will stop receiving async notifications.
Note:Note that this function may also return error codes from previous, asynchronous launches.
See also:
Returns which device is currently being used.
Returns the number of compute-capable devices.
Gets the flags for the current device.
Returns in flags the flags for the current device. If there is a current device for the calling thread, the flags for the device are returned. If there is no current device, the flags for the first device are returned, which may be the default flags. Compare to the behavior of cudaSetDeviceFlags.
Typically, the flags returned should match the behavior that will be seen if the calling thread uses a device after this call, without any change to the flags or current device inbetween by this or another thread. Note that if the device is not initialized, it is possible for another thread to change the flags for the current device before it is initialized. Additionally, when using exclusive mode, if this thread has not requested a specific device, it may use a device other than the first device, contrary to the assumption made by this function.
If a context has been created via the driver API and is current to the calling thread, the flags for that context are always returned.
Flags returned by this function may specifically include cudaDeviceMapHost even though it is not accepted by cudaSetDeviceFlags because it is implicit in runtime API flags. The reason for this is that the current context may have been created via the driver API in which case the flag is not implicit and may be unset.
See also:
cudaGetDevice, cudaGetDeviceProperties, cudaSetDevice, cudaSetDeviceFlags, cudaInitDevice, cuCtxGetFlags, cuDevicePrimaryCtxGetState
Returns information about the compute-device.
Initialize device to be used for GPU executions.
Attempts to close memory mapped with cudaIpcOpenMemHandle.
Gets an interprocess handle for a previously allocated event.
Takes as input a previously allocated event. This event must have been created with the cudaEventInterprocess and cudaEventDisableTiming flags set. This opaque handle may be copied into other processes and opened with cudaIpcOpenEventHandle to allow efficient hardware synchronization between GPU work in different processes.
After the event has been been opened in the importing process, cudaEventRecord, cudaEventSynchronize, cudaStreamWaitEvent and cudaEventQuery may be used in either process. Performing operations on the imported event after the exported event has been freed with cudaEventDestroy will result in undefined behavior.
IPC functionality is restricted to devices with support for unified addressing on Linux and Windows operating systems. IPC functionality on Windows is supported for compatibility purposes but not recommended as it comes with performance cost. Users can test their device for IPC functionality by calling cudaDeviceGetAttribute with cudaDevAttrIpcEventSupport
See also:
cudaEventCreate, cudaEventDestroy, cudaEventSynchronize, cudaEventQuery, cudaStreamWaitEvent, cudaIpcOpenEventHandle, cudaIpcGetMemHandle, cudaIpcOpenMemHandle, cudaIpcCloseMemHandle, cuIpcGetEventHandle
Gets an interprocess memory handle for an existing device memory allocation.
Opens an interprocess event handle for use in the current process.
Opens an interprocess memory handle exported from another process and returns a device pointer usable in the local process.
Set device to be used for GPU executions.
Sets device as the current device for the calling host thread. Valid device id's are 0 to (cudaGetDeviceCount() - 1).
Any device memory subsequently allocated from this host thread using cudaMalloc(), cudaMallocPitch() or cudaMallocArray() will be physically resident on device. Any host memory allocated from this host thread using cudaMallocHost() or cudaHostAlloc() or cudaHostRegister() will have its lifetime associated with device. Any streams or events created from this host thread will be associated with device. Any kernels launched from this host thread using the <<<>>> operator or cudaLaunchKernel() will be executed on device.
This call may be made from any host thread, to any device, and at any time. This function will do no synchronization with the previous or new device, and should only take significant time when it initializes the runtime's context state. This call will bind the primary context of the specified device to the calling thread and all the subsequent memory allocations, stream and event creations, and kernel launches will be associated with the primary context. This function will also immediately initialize the runtime state on the primary context, and the context will be current on device immediately. This function will return an error if the device is in cudaComputeModeExclusiveProcess and is occupied by another process or if the device is in cudaComputeModeProhibited.
It is not required to call cudaInitDevice before using this function.
See also:
cudaGetDeviceCount, cudaGetDevice, cudaGetDeviceProperties, cudaChooseDevice, cudaInitDevice, cuCtxSetCurrent
Sets flags to be used for device executions.
Records flags as the flags for the current device. If the current device has been set and that device has already been initialized, the previous flags are overwritten. If the current device has not been initialized, it is initialized with the provided flags. If no device has been made current to the calling thread, a default device is selected and initialized with the provided flags.
The three LSBs of the flags parameter can be used to control how the CPU thread interacts with the OS scheduler when waiting for results from the device.
cudaDeviceScheduleAuto: The default value if the flags parameter is zero, uses a heuristic based on the number of active CUDA contexts in the process C and the number of logical processors in the system P. If C > P, then CUDA will yield to other OS threads when waiting for the device, otherwise CUDA will not yield while waiting for results and actively spin on the processor. Additionally, on Tegra devices, cudaDeviceScheduleAuto uses a heuristic based on the power profile of the platform and may choose cudaDeviceScheduleBlockingSync for low-powered devices.
cudaDeviceScheduleSpin: Instruct CUDA to actively spin when waiting for results from the device. This can decrease latency when waiting for the device, but may lower the performance of CPU threads if they are performing work in parallel with the CUDA thread.
cudaDeviceScheduleYield: Instruct CUDA to yield its thread when waiting for results from the device. This can increase latency when waiting for the device, but can increase the performance of CPU threads performing work in parallel with the device.
cudaDeviceScheduleBlockingSync: Instruct CUDA to block the CPU thread on a synchronization primitive when waiting for the device to finish work.
cudaDeviceBlockingSync: Instruct CUDA to block the CPU thread on a synchronization primitive when waiting for the device to finish work.
Deprecated: This flag was deprecated as of CUDA 4.0 and replaced with cudaDeviceScheduleBlockingSync.
cudaDeviceMapHost: This flag enables allocating pinned host memory that is accessible to the device. It is implicit for the runtime but may be absent if a context is created using the driver API. If this flag is not set, cudaHostGetDevicePointer() will always return a failure code.
cudaDeviceLmemResizeToMax: Instruct CUDA to not reduce local memory after resizing local memory for a kernel. This can prevent thrashing by local memory allocations when launching many kernels with high local memory usage at the cost of potentially increased memory usage.
Deprecated: This flag is deprecated and the behavior enabled by this flag is now the default and cannot be disabled.
cudaDeviceSyncMemops: Ensures that synchronous memory operations initiated on this context will always synchronize. See further documentation in the section titled "API Synchronization behavior" to learn more about cases when synchronous memory operations can exhibit asynchronous behavior.
See also:
cudaGetDeviceFlags, cudaGetDeviceCount, cudaGetDevice, cudaGetDeviceProperties, cudaSetDevice, cudaSetValidDevices, cudaInitDevice, cudaChooseDevice, cuDevicePrimaryCtxSetFlags
Set a list of devices that can be used for CUDA.
Sets a list of devices for CUDA execution in priority order using device_arr. The parameter len specifies the number of elements in the list. CUDA will try devices from the list sequentially until it finds one that works. If this function is not called, or if it is called with a len of 0, then CUDA will go back to its default behavior of trying devices sequentially from a default list containing all of the available CUDA devices in the system. If a specified device ID in the list does not exist, this function will return cudaErrorInvalidDevice. If len is not 0 and device_arr is NULL or if len exceeds the number of devices in the system, then cudaErrorInvalidValue is returned.
See also:
cudaGetDeviceCount, cudaSetDevice, cudaGetDeviceProperties, cudaSetDeviceFlags, cudaChooseDevice
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