C++ API Routines

template<class T , int dim> cudaError_t cudaBindSurfaceToArray (const struct surface< T, dim > & surf, cudaArray_const_t array)

Binds the CUDA array array to the surface reference surf. The channel descriptor is inherited from the CUDA array. Any CUDA array previously bound to surf is unbound.

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template<class T , int dim> cudaError_t cudaBindSurfaceToArray (const struct surface< T, dim > & surf, cudaArray_const_t array, const struct cudaChannelFormatDesc & desc)

Binds the CUDA array array to the surface reference surf. desc describes how the memory is interpreted when dealing with the surface. Any CUDA array previously bound to surf is unbound.

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template<class T , int dim, enum cudaTextureReadMode readMode> cudaError_t cudaBindTexture (size_t * offset, const struct texture< T, dim, readMode > & tex, const void * devPtr, size_t size = UINT_MAX)

Binds size bytes of the memory area pointed to by devPtr to texture reference tex. The channel descriptor is inherited from the texture reference type. The offset parameter is an optional byte offset as with the low-level cudaBindTexture(size_t*, const struct textureReference*, const void*, const struct cudaChannelFormatDesc*, size_t) function. Any memory previously bound to tex is unbound.

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template<class T , int dim, enum cudaTextureReadMode readMode> cudaError_t cudaBindTexture (size_t * offset, const struct texture< T, dim, readMode > & tex, const void * devPtr, const struct cudaChannelFormatDesc & desc, size_t size = UINT_MAX)

Binds size bytes of the memory area pointed to by devPtr to texture reference tex. desc describes how the memory is interpreted when fetching values from the texture. The offset parameter is an optional byte offset as with the low-level cudaBindTexture() function. Any memory previously bound to tex is unbound.

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template<class T , int dim, enum cudaTextureReadMode readMode> cudaError_t cudaBindTexture2D (size_t * offset, const struct texture< T, dim, readMode > & tex, const void * devPtr, size_t width, size_t height, size_t pitch)

Binds the 2D memory area pointed to by devPtr to the texture reference tex. The size of the area is constrained by width in texel units, height in texel units, and pitch in byte units. The channel descriptor is inherited from the texture reference type. Any memory previously bound to tex is unbound.

Since the hardware enforces an alignment requirement on texture base addresses, cudaBindTexture2D() returns in *offset a byte offset that must be applied to texture fetches in order to read from the desired memory. This offset must be divided by the texel size and passed to kernels that read from the texture so they can be applied to the tex2D() function. If the device memory pointer was returned from cudaMalloc(), the offset is guaranteed to be 0 and NULL may be passed as the offset parameter.

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template<class T , int dim, enum cudaTextureReadMode readMode> cudaError_t cudaBindTexture2D (size_t * offset, const struct texture< T, dim, readMode > & tex, const void * devPtr, const struct cudaChannelFormatDesc & desc, size_t width, size_t height, size_t pitch)

Binds the 2D memory area pointed to by devPtr to the texture reference tex. The size of the area is constrained by width in texel units, height in texel units, and pitch in byte units. desc describes how the memory is interpreted when fetching values from the texture. Any memory previously bound to tex is unbound.

Since the hardware enforces an alignment requirement on texture base addresses, cudaBindTexture2D() returns in *offset a byte offset that must be applied to texture fetches in order to read from the desired memory. This offset must be divided by the texel size and passed to kernels that read from the texture so they can be applied to the tex2D() function. If the device memory pointer was returned from cudaMalloc(), the offset is guaranteed to be 0 and NULL may be passed as the offset parameter.

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template<class T , int dim, enum cudaTextureReadMode readMode> cudaError_t cudaBindTextureToArray (const struct texture< T, dim, readMode > & tex, cudaArray_const_t array)

Binds the CUDA array array to the texture reference tex. The channel descriptor is inherited from the CUDA array. Any CUDA array previously bound to tex is unbound.

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template<class T , int dim, enum cudaTextureReadMode readMode> cudaError_t cudaBindTextureToArray (const struct texture< T, dim, readMode > & tex, cudaArray_const_t array, const struct cudaChannelFormatDesc & desc)

Binds the CUDA array array to the texture reference tex. desc describes how the memory is interpreted when fetching values from the texture. Any CUDA array previously bound to tex is unbound.

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template<class T , int dim, enum cudaTextureReadMode readMode> cudaError_t cudaBindTextureToMipmappedArray (const struct texture< T, dim, readMode > & tex, cudaMipmappedArray_const_t mipmappedArray)

Binds the CUDA mipmapped array mipmappedArray to the texture reference tex. The channel descriptor is inherited from the CUDA array. Any CUDA mipmapped array previously bound to tex is unbound.

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template<class T , int dim, enum cudaTextureReadMode readMode> cudaError_t cudaBindTextureToMipmappedArray (const struct texture< T, dim, readMode > & tex, cudaMipmappedArray_const_t mipmappedArray, const struct cudaChannelFormatDesc & desc)

Binds the CUDA mipmapped array mipmappedArray to the texture reference tex. desc describes how the memory is interpreted when fetching values from the texture. Any CUDA mipmapped array previously bound to tex is unbound.

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template<class T > cudaChannelFormatDesc cudaCreateChannelDesc (void)

Returns a channel descriptor with format f and number of bits of each component x, y, z, and w. The cudaChannelFormatDesc is defined as:

  struct cudaChannelFormatDesc {
    int x, y, z, w;
    enum cudaChannelFormatKind f;
  };

where cudaChannelFormatKind is one of cudaChannelFormatKindSigned, cudaChannelFormatKindUnsigned, or cudaChannelFormatKindFloat.

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cudaError_t cudaEventCreate (cudaEvent_t * event, unsigned int flags)

Creates an event object with the specified flags. Valid flags include:

• cudaEventDefault: Default event creation flag.
• cudaEventBlockingSync: Specifies that event should use blocking synchronization. A host thread that uses cudaEventSynchronize() to wait on an event created with this flag will block until the event actually completes.
• cudaEventDisableTiming: Specifies that the created event does not need to record timing data. Events created with this flag specified and the cudaEventBlockingSync flag not specified will provide the best performance when used with cudaStreamWaitEvent() and cudaEventQuery().

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template<class T > cudaError_t cudaFuncGetAttributes (struct cudaFuncAttributes * attr, T * entry)

This function obtains the attributes of a function specified via entry. The parameter entry must be a pointer to a function that executes on the device. The parameter specified by entry must be declared as a __global__ function. The fetched attributes are placed in attr. If the specified function does not exist, then cudaErrorInvalidDeviceFunction is returned.

Note that some function attributes such as maxThreadsPerBlock may vary based on the device that is currently being used.

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cudaLaunchKernel (C++ API), cudaFuncSetCacheConfig (C++ API), cudaFuncGetAttributes (C API), cudaSetDoubleForDevice, cudaSetDoubleForHost, cudaSetupArgument (C++ API)

template<class T > cudaError_t cudaFuncSetCacheConfig (T * func, enum cudaFuncCache cacheConfig)

On devices where the L1 cache and shared memory use the same hardware resources, this sets through cacheConfig the preferred cache configuration for the function specified via func. This is only a preference. The runtime will use the requested configuration if possible, but it is free to choose a different configuration if required to execute func.

func must be a pointer to a function that executes on the device. The parameter specified by func must be declared as a __global__ function. If the specified function does not exist, then cudaErrorInvalidDeviceFunction is returned.

This setting does nothing on devices where the size of the L1 cache and shared memory are fixed.

Launching a kernel with a different preference than the most recent preference setting may insert a device-side synchronization point.

The supported cache configurations are:

• cudaFuncCachePreferNone: no preference for shared memory or L1 (default)
• cudaFuncCachePreferShared: prefer larger shared memory and smaller L1 cache
• cudaFuncCachePreferL1: prefer larger L1 cache and smaller shared memory

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cudaLaunchKernel (C++ API), cudaFuncSetCacheConfig (C API), cudaFuncGetAttributes (C++ API), cudaSetDoubleForDevice, cudaSetDoubleForHost, cudaSetupArgument (C++ API), cudaThreadGetCacheConfig, cudaThreadSetCacheConfig

template<class T > cudaError_t cudaGetSymbolAddress (void ** devPtr, const T & symbol)

Returns in *devPtr the address of symbol symbol on the device. symbol can either be a variable that resides in global or constant memory space. If symbol cannot be found, or if symbol is not declared in the global or constant memory space, *devPtr is unchanged and the error cudaErrorInvalidSymbol is returned.

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template<class T > cudaError_t cudaGetSymbolSize (size_t * size, const T & symbol)

Returns in *size the size of symbol symbol. symbol must be a variable that resides in global or constant memory space. If symbol cannot be found, or if symbol is not declared in global or constant memory space, *size is unchanged and the error cudaErrorInvalidSymbol is returned.

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template<class T , int dim, enum cudaTextureReadMode readMode> cudaError_t cudaGetTextureAlignmentOffset (size_t * offset, const struct texture< T, dim, readMode > & tex)

Returns in *offset the offset that was returned when texture reference tex was bound.

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template<class T > cudaError_t cudaLaunch (T * func)

Returns in *minGridSize and *blocksize a suggested grid / block size pair that achieves the best potential occupancy (i.e. the maximum number of active warps with the smallest number of blocks).

The flags parameter controls how special cases are handled. Valid flags include:

• cudaOccupancyDefault: keeps the default behavior as cudaOccupancyMaxPotentialBlockSizeVariableSMemWithFlags
• cudaOccupancyDisableCachingOverride: This flag suppresses the default behavior on platform where global caching affects occupancy. On such platforms, if caching is enabled, but per-block SM resource usage would result in zero occupancy, the occupancy calculator will calculate the occupancy as if caching is disabled. Setting this flag makes the occupancy calculator to return 0 in such cases. More information can be found about this feature in the 'Unified L1/Texture Cache' section of the Maxwell tuning guide.

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Deprecated

Launches the function entry on the device. The parameter entry must be a function that executes on the device. The parameter specified by entry must be declared as a __global__ function. cudaLaunch() must be preceded by a call to cudaConfigureCall() since it pops the data that was pushed by cudaConfigureCall() from the execution stack.

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cudaLaunchKernel (C++ API), cudaFuncSetCacheConfig (C++ API), cudaFuncGetAttributes (C++ API), cudaLaunch (C API), cudaSetDoubleForDevice, cudaSetDoubleForHost, cudaSetupArgument (C++ API), cudaThreadGetCacheConfig, cudaThreadSetCacheConfig

template<class T > cudaError_t cudaLaunchKernel (const T * func, dim3 gridDim, dim3 blockDim, void ** args, size_t sharedMem = 0, cudaStream_t stream = 0)

The function invokes kernel func on gridDim (gridDim.x × gridDim.y × gridDim.z) grid of blocks. Each block contains blockDim (blockDim.x × blockDim.y × blockDim.z) threads.

If the kernel has N parameters the args should point to array of N pointers. Each pointer, from args[0] to args[N - 1], point to the region of memory from which the actual parameter will be copied.

sharedMem sets the amount of dynamic shared memory that will be available to each thread block.

stream specifies a stream the invocation is associated to.

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cudaLaunchKernel (C API)

cudaError_t cudaMallocHost (void ** ptr, size_t size, unsigned int flags)

Allocates size bytes of host memory that is page-locked and accessible to the device. The driver tracks the virtual memory ranges allocated with this function and automatically accelerates calls to functions such as cudaMemcpy(). Since the memory can be accessed directly by the device, it can be read or written with much higher bandwidth than pageable memory obtained with functions such as malloc(). Allocating excessive amounts of pinned memory may degrade system performance, since it reduces the amount of memory available to the system for paging. As a result, this function is best used sparingly to allocate staging areas for data exchange between host and device.

The flags parameter enables different options to be specified that affect the allocation, as follows.

• cudaHostAllocDefault: This flag's value is defined to be 0.
• cudaHostAllocPortable: The memory returned by this call will be considered as pinned memory by all CUDA contexts, not just the one that performed the allocation.
• cudaHostAllocMapped: Maps the allocation into the CUDA address space. The device pointer to the memory may be obtained by calling cudaHostGetDevicePointer().
• cudaHostAllocWriteCombined: Allocates the memory as write-combined (WC). WC memory can be transferred across the PCI Express bus more quickly on some system configurations, but cannot be read efficiently by most CPUs. WC memory is a good option for buffers that will be written by the CPU and read by the device via mapped pinned memory or host->device transfers.

All of these flags are orthogonal to one another: a developer may allocate memory that is portable, mapped and/or write-combined with no restrictions.

cudaSetDeviceFlags() must have been called with the cudaDeviceMapHost flag in order for the cudaHostAllocMapped flag to have any effect.

The cudaHostAllocMapped flag may be specified on CUDA contexts for devices that do not support mapped pinned memory. The failure is deferred to cudaHostGetDevicePointer() because the memory may be mapped into other CUDA contexts via the cudaHostAllocPortable flag.

Memory allocated by this function must be freed with cudaFreeHost().

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template<class T > cudaError_t cudaMallocManaged (T ** devPtr, size_t size, unsigned int flags = cudaMemAttachGlobal)

Allocates size bytes of managed memory on the device and returns in *devPtr a pointer to the allocated memory. If the device doesn't support allocating managed memory, cudaErrorNotSupported is returned. Support for managed memory can be queried using the device attribute cudaDevAttrManagedMemory. The allocated memory is suitably aligned for any kind of variable. The memory is not cleared. If size is 0, cudaMallocManaged returns cudaErrorInvalidValue. The pointer is valid on the CPU and on all GPUs in the system that support managed memory. All accesses to this pointer must obey the Unified Memory programming model.

flags specifies the default stream association for this allocation. flags must be one of cudaMemAttachGlobal or cudaMemAttachHost. The default value for flags is cudaMemAttachGlobal. If cudaMemAttachGlobal is specified, then this memory is accessible from any stream on any device. If cudaMemAttachHost is specified, then the allocation is created with initial visibility restricted to host access only; an explicit call to cudaStreamAttachMemAsync will be required to enable access on the device.

If the association is later changed via cudaStreamAttachMemAsync to a single stream, the default association, as specified during cudaMallocManaged, is restored when that stream is destroyed. For __managed__ variables, the default association is always cudaMemAttachGlobal. Note that destroying a stream is an asynchronous operation, and as a result, the change to default association won't happen until all work in the stream has completed.

Memory allocated with cudaMallocManaged should be released with cudaFree.

On a multi-GPU system with peer-to-peer support, where multiple GPUs support managed memory, the physical storage is created on the GPU which is active at the time cudaMallocManaged is called. All other GPUs will reference the data at reduced bandwidth via peer mappings over the PCIe bus. The Unified Memory management system does not migrate memory between GPUs.

On a multi-GPU system where multiple GPUs support managed memory, but not all pairs of such GPUs have peer-to-peer support between them, the physical storage is created in 'zero-copy' or system memory. All GPUs will reference the data at reduced bandwidth over the PCIe bus. In these circumstances, use of the environment variable, CUDA_VISIBLE_DEVICES, is recommended to restrict CUDA to only use those GPUs that have peer-to-peer support. Alternatively, users can also set CUDA_MANAGED_FORCE_DEVICE_ALLOC to a non-zero value to force the driver to always use device memory for physical storage. When this environment variable is set to a non-zero value, all devices used in that process that support managed memory have to be peer-to-peer compatible with each other. The error cudaErrorInvalidDevice will be returned if a device that supports managed memory is used and it is not peer-to-peer compatible with any of the other managed memory supporting devices that were previously used in that process, even if cudaDeviceReset has been called on those devices. These environment variables are described in the CUDA programming guide under the 'CUDA environment variables' section.

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template<class T > cudaError_t cudaMemcpyFromSymbol (void * dst, const T & symbol, size_t count, size_t offset = 0, enum cudaMemcpyKind kind = cudaMemcpyDeviceToHost)

Copies count bytes from the memory area offset bytes from the start of symbol symbol to the memory area pointed to by dst. The memory areas may not overlap. symbol is a variable that resides in global or constant memory space. kind can be either cudaMemcpyDeviceToHost or cudaMemcpyDeviceToDevice.

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template<class T > cudaError_t cudaMemcpyFromSymbolAsync (void * dst, const T & symbol, size_t count, size_t offset = 0, enum cudaMemcpyKind kind = cudaMemcpyDeviceToHost, cudaStream_t stream = 0)

Copies count bytes from the memory area offset bytes from the start of symbol symbol to the memory area pointed to by dst. The memory areas may not overlap. symbol is a variable that resides in global or constant memory space. kind can be either cudaMemcpyDeviceToHost or cudaMemcpyDeviceToDevice.

cudaMemcpyFromSymbolAsync() is asynchronous with respect to the host, so the call may return before the copy is complete. The copy can optionally be associated to a stream by passing a non-zero stream argument. If kind is cudaMemcpyDeviceToHost and stream is non-zero, the copy may overlap with operations in other streams.

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template<class T > cudaError_t cudaMemcpyToSymbol (const T & symbol, const void * src, size_t count, size_t offset = 0, enum cudaMemcpyKind kind = cudaMemcpyHostToDevice)

Copies count bytes from the memory area pointed to by src to the memory area offset bytes from the start of symbol symbol. The memory areas may not overlap. symbol is a variable that resides in global or constant memory space. kind can be either cudaMemcpyHostToDevice or cudaMemcpyDeviceToDevice.

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template<class T > cudaError_t cudaMemcpyToSymbolAsync (const T & symbol, const void * src, size_t count, size_t offset = 0, enum cudaMemcpyKind kind = cudaMemcpyHostToDevice, cudaStream_t stream = 0)

Copies count bytes from the memory area pointed to by src to the memory area offset bytes from the start of symbol symbol. The memory areas may not overlap. symbol is a variable that resides in global or constant memory space. kind can be either cudaMemcpyHostToDevice or cudaMemcpyDeviceToDevice.

cudaMemcpyToSymbolAsync() is asynchronous with respect to the host, so the call may return before the copy is complete. The copy can optionally be associated to a stream by passing a non-zero stream argument. If kind is cudaMemcpyHostToDevice and stream is non-zero, the copy may overlap with operations in other streams.

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template<class T > cudaError_t cudaOccupancyMaxActiveBlocksPerMultiprocessor (int * numBlocks, T func, int blockSize, size_t dynamicSMemSize)

Returns in *numBlocks the maximum number of active blocks per streaming multiprocessor for the device function.

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template<class T > cudaError_t cudaOccupancyMaxActiveBlocksPerMultiprocessorWithFlags (int * numBlocks, T func, int blockSize, size_t dynamicSMemSize, unsigned int flags)

Returns in *numBlocks the maximum number of active blocks per streaming multiprocessor for the device function.

The flags parameter controls how special cases are handled. Valid flags include:

• cudaOccupancyDefault: keeps the default behavior as cudaOccupancyMaxActiveBlocksPerMultiprocessor
• cudaOccupancyDisableCachingOverride: suppresses the default behavior on platform where global caching affects occupancy. On such platforms, if caching is enabled, but per-block SM resource usage would result in zero occupancy, the occupancy calculator will calculate the occupancy as if caching is disabled. Setting this flag makes the occupancy calculator to return 0 in such cases. More information can be found about this feature in the 'Unified L1/Texture Cache' section of the Maxwell tuning guide.

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template<class T > CUDART_DEVICE cudaError_t cudaOccupancyMaxPotentialBlockSize (int * minGridSize, int * blockSize, T func, size_t dynamicSMemSize = 0, int blockSizeLimit = 0)

Returns in *minGridSize and *blocksize a suggested grid / block size pair that achieves the best potential occupancy (i.e. the maximum number of active warps with the smallest number of blocks).

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template<typename UnaryFunction , class T > CUDART_DEVICE cudaError_t cudaOccupancyMaxPotentialBlockSizeVariableSMem (int * minGridSize, int * blockSize, T func, UnaryFunction blockSizeToDynamicSMemSize, int blockSizeLimit = 0)

Returns in *minGridSize and *blocksize a suggested grid / block size pair that achieves the best potential occupancy (i.e. the maximum number of active warps with the smallest number of blocks).

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template<class T > CUDART_DEVICE cudaError_t cudaOccupancyMaxPotentialBlockSizeWithFlags (int * minGridSize, int * blockSize, T func, size_t dynamicSMemSize = 0, int blockSizeLimit = 0, unsigned int flags = 0)

Returns in *minGridSize and *blocksize a suggested grid / block size pair that achieves the best potential occupancy (i.e. the maximum number of active warps with the smallest number of blocks).

The flags parameter controls how special cases are handle. Valid flags include:

• cudaOccupancyDefault: keeps the default behavior as cudaOccupancyMaxPotentialBlockSize
• cudaOccupancyDisableCachingOverride: This flag suppresses the default behavior on platform where global caching affects occupancy. On such platforms, if caching is enabled, but per-block SM resource usage would result in zero occupancy, the occupancy calculator will calculate the occupancy as if caching is disabled. Setting this flag makes the occupancy calculator to return 0 in such cases. More information can be found about this feature in the 'Unified L1/Texture Cache' section of the Maxwell tuning guide.

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template<class T > cudaError_t cudaSetupArgument (T arg, size_t offset)

Deprecated

Pushes size bytes of the argument pointed to by arg at offset bytes from the start of the parameter passing area, which starts at offset 0. The arguments are stored in the top of the execution stack. cudaSetupArgument() must be preceded by a call to cudaConfigureCall().

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cudaLaunchKernel (C++ API), cudaFuncGetAttributes (C++ API), cudaLaunch (C++ API), cudaSetDoubleForDevice, cudaSetDoubleForHost, cudaSetupArgument (C API)

template<class T > cudaError_t cudaStreamAttachMemAsync (cudaStream_t stream, T * devPtr, size_t length = 0, unsigned int flags = cudaMemAttachSingle)

Enqueues an operation in stream to specify stream association of length bytes of memory starting from devPtr. This function is a stream-ordered operation, meaning that it is dependent on, and will only take effect when, previous work in stream has completed. Any previous association is automatically replaced.

devPtr must point to an address within managed memory space declared using the __managed__ keyword or allocated with cudaMallocManaged.

length must be zero, to indicate that the entire allocation's stream association is being changed. Currently, it's not possible to change stream association for a portion of an allocation. The default value for length is zero.

The stream association is specified using flags which must be one of cudaMemAttachGlobal, cudaMemAttachHost or cudaMemAttachSingle. The default value for flags is cudaMemAttachSingle If the cudaMemAttachGlobal flag is specified, the memory can be accessed by any stream on any device. If the cudaMemAttachHost flag is specified, the program makes a guarantee that it won't access the memory on the device from any stream. If the cudaMemAttachSingle flag is specified, the program makes a guarantee that it will only access the memory on the device from stream. It is illegal to attach singly to the NULL stream, because the NULL stream is a virtual global stream and not a specific stream. An error will be returned in this case.

When memory is associated with a single stream, the Unified Memory system will allow CPU access to this memory region so long as all operations in stream have completed, regardless of whether other streams are active. In effect, this constrains exclusive ownership of the managed memory region by an active GPU to per-stream activity instead of whole-GPU activity.

Accessing memory on the device from streams that are not associated with it will produce undefined results. No error checking is performed by the Unified Memory system to ensure that kernels launched into other streams do not access this region.

It is a program's responsibility to order calls to cudaStreamAttachMemAsync via events, synchronization or other means to ensure legal access to memory at all times. Data visibility and coherency will be changed appropriately for all kernels which follow a stream-association change.

If stream is destroyed while data is associated with it, the association is removed and the association reverts to the default visibility of the allocation as specified at cudaMallocManaged. For __managed__ variables, the default association is always cudaMemAttachGlobal. Note that destroying a stream is an asynchronous operation, and as a result, the change to default association won't happen until all work in the stream has completed.

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template<class T , int dim, enum cudaTextureReadMode readMode> cudaError_t cudaUnbindTexture (const struct texture< T, dim, readMode > & tex)

Unbinds the texture bound to tex.

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