6.27. Occupancy

This section describes the occupancy calculation functions of the CUDA runtime application programming interface.

Besides the occupancy calculator functions (cudaOccupancyMaxActiveBlocksPerMultiprocessor and cudaOccupancyMaxActiveBlocksPerMultiprocessorWithFlags), there are also C++ only occupancy-based launch configuration functions documented in C++ API Routines module.

See cudaOccupancyMaxPotentialBlockSize (C++ API), cudaOccupancyMaxPotentialBlockSize (C++ API), cudaOccupancyMaxPotentialBlockSizeVariableSMem (C++ API), cudaOccupancyMaxPotentialBlockSizeVariableSMem (C++ API) cudaOccupancyAvailableDynamicSMemPerBlock (C++ API),

Functions

__host__ cudaError_t cudaOccupancyAvailableDynamicSMemPerBlock(size_t *dynamicSmemSize, const void *func, int numBlocks, int blockSize): Returns dynamic shared memory available per block when launching numBlocks blocks on SM.
__host__ cudaError_t cudaOccupancyMaxActiveBlocksPerMultiprocessor(int *numBlocks, const void *func, int blockSize, size_t dynamicSMemSize): Returns occupancy for a device function.
__host__ cudaError_t cudaOccupancyMaxActiveBlocksPerMultiprocessorWithFlags(int *numBlocks, const void *func, int blockSize, size_t dynamicSMemSize, unsigned int flags): Returns occupancy for a device function with the specified flags.
__host__ cudaError_t cudaOccupancyMaxActiveClusters(int *numClusters, const void *func, const cudaLaunchConfig_t *launchConfig): Given the kernel function ( func ) and launch configuration ( config ), return the maximum number of clusters that could co-exist on the target device in *numClusters .
__host__ cudaError_t cudaOccupancyMaxPotentialClusterSize(int *clusterSize, const void *func, const cudaLaunchConfig_t *launchConfig): Given the kernel function ( func ) and launch configuration ( config ), return the maximum cluster size in *clusterSize .

6.27.1. Functions

__host__ cudaError_t cudaOccupancyAvailableDynamicSMemPerBlock(size_t *dynamicSmemSize, const void *func, int numBlocks, int blockSize)

Returns dynamic shared memory available per block when launching numBlocks blocks on SM.

Returns in *dynamicSmemSize the maximum size of dynamic shared memory to allow numBlocks blocks per SM.

Note

Note that this function may also return error codes from previous, asynchronous launches.

Note

Note that this function may also return cudaErrorInitializationError, cudaErrorInsufficientDriver or cudaErrorNoDevice if this call tries to initialize internal CUDA RT state.

Note

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.

Note

The API can also be used with a kernel cudaKernel_t by querying the handle using cudaLibraryGetKernel() or cudaGetKernel and then passing it to the API by casting to void*. The symbol entryFuncAddr passed to cudaGetKernel should be a symbol that is registered with the same CUDA Runtime instance.

Note

Passing a symbol that belongs that belongs to a different runtime instance will result in undefined behavior. The only type that can be reliably passed to a different runtime instance is cudaKernel_t

Parameters

dynamicSmemSize – - Returned maximum dynamic shared memory
func – - Kernel function for which occupancy is calculated
numBlocks – - Number of blocks to fit on SM
blockSize – - Size of the block

Returns

cudaSuccess, cudaErrorInvalidDevice, cudaErrorInvalidDeviceFunction, cudaErrorInvalidValue, cudaErrorUnknown,

__host__ cudaError_t cudaOccupancyMaxActiveBlocksPerMultiprocessor(int *numBlocks, const void *func, int blockSize, size_t dynamicSMemSize)

Returns occupancy for a device function.

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

Note

Note that this function may also return error codes from previous, asynchronous launches.

Note

Note that this function may also return cudaErrorInitializationError, cudaErrorInsufficientDriver or cudaErrorNoDevice if this call tries to initialize internal CUDA RT state.

Note

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.

Note

The API can also be used with a kernel cudaKernel_t by querying the handle using cudaLibraryGetKernel() or cudaGetKernel and then passing it to the API by casting to void*. The symbol entryFuncAddr passed to cudaGetKernel should be a symbol that is registered with the same CUDA Runtime instance.

Note

Passing a symbol that belongs that belongs to a different runtime instance will result in undefined behavior. The only type that can be reliably passed to a different runtime instance is cudaKernel_t

Parameters

numBlocks – - Returned occupancy
func – - Kernel function for which occupancy is calculated
blockSize – - Block size the kernel is intended to be launched with
dynamicSMemSize – - Per-block dynamic shared memory usage intended, in bytes

Returns

cudaSuccess, cudaErrorInvalidDevice, cudaErrorInvalidDeviceFunction, cudaErrorInvalidValue, cudaErrorUnknown,

__host__ cudaError_t cudaOccupancyMaxActiveBlocksPerMultiprocessorWithFlags(int *numBlocks, const void *func, int blockSize, size_t dynamicSMemSize, unsigned int flags)

Returns occupancy for a device function with the specified 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: 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.

See also

cudaOccupancyMaxActiveBlocksPerMultiprocessor, cudaOccupancyMaxPotentialBlockSize (C++ API), cudaOccupancyMaxPotentialBlockSizeWithFlags (C++ API), cudaOccupancyMaxPotentialBlockSizeVariableSMem (C++ API), cudaOccupancyMaxPotentialBlockSizeVariableSMemWithFlags (C++ API), cudaOccupancyAvailableDynamicSMemPerBlock (C++ API), ::cuOccupancyMaxActiveBlocksPerMultiprocessorWithFlags

Note

Note that this function may also return error codes from previous, asynchronous launches.

Note

Note that this function may also return cudaErrorInitializationError, cudaErrorInsufficientDriver or cudaErrorNoDevice if this call tries to initialize internal CUDA RT state.

Note

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.

Note

The API can also be used with a kernel cudaKernel_t by querying the handle using cudaLibraryGetKernel() or cudaGetKernel and then passing it to the API by casting to void*. The symbol entryFuncAddr passed to cudaGetKernel should be a symbol that is registered with the same CUDA Runtime instance.

Note

Passing a symbol that belongs that belongs to a different runtime instance will result in undefined behavior. The only type that can be reliably passed to a different runtime instance is cudaKernel_t

Parameters

numBlocks – - Returned occupancy
func – - Kernel function for which occupancy is calculated
blockSize – - Block size the kernel is intended to be launched with
dynamicSMemSize – - Per-block dynamic shared memory usage intended, in bytes
flags – - Requested behavior for the occupancy calculator

Returns

cudaSuccess, cudaErrorInvalidDevice, cudaErrorInvalidDeviceFunction, cudaErrorInvalidValue, cudaErrorUnknown,

__host__ cudaError_t cudaOccupancyMaxActiveClusters(int *numClusters, const void *func, const cudaLaunchConfig_t *launchConfig)

Given the kernel function (func) and launch configuration (config), return the maximum number of clusters that could co-exist on the target device in *numClusters.

If the function has required cluster size already set (see cudaFuncGetAttributes), the cluster size from config must either be unspecified or match the required size. Without required sizes, the cluster size must be specified in config, else the function will return an error.

Note that various attributes of the kernel function may affect occupancy calculation. Runtime environment may affect how the hardware schedules the clusters, so the calculated occupancy is not guaranteed to be achievable.

See also

cudaFuncGetAttributes cudaOccupancyMaxActiveClusters (C++ API), ::cuOccupancyMaxActiveClusters

Note

Note that this function may also return error codes from previous, asynchronous launches.

Note

Note that this function may also return cudaErrorInitializationError, cudaErrorInsufficientDriver or cudaErrorNoDevice if this call tries to initialize internal CUDA RT state.

Note

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.

Note

The API can also be used with a kernel cudaKernel_t by querying the handle using cudaLibraryGetKernel() or cudaGetKernel and then passing it to the API by casting to void*. The symbol entryFuncAddr passed to cudaGetKernel should be a symbol that is registered with the same CUDA Runtime instance.

Note

Passing a symbol that belongs that belongs to a different runtime instance will result in undefined behavior. The only type that can be reliably passed to a different runtime instance is cudaKernel_t

Parameters

numClusters – - Returned maximum number of clusters that could co-exist on the target device
func – - Kernel function for which maximum number of clusters are calculated
config – - Launch configuration for the given kernel function

Returns

cudaSuccess, cudaErrorInvalidDeviceFunction, cudaErrorInvalidValue, cudaErrorInvalidClusterSize, cudaErrorUnknown,

__host__ cudaError_t cudaOccupancyMaxPotentialClusterSize(int *clusterSize, const void *func, const cudaLaunchConfig_t *launchConfig)

Given the kernel function (func) and launch configuration (config), return the maximum cluster size in *clusterSize.

The cluster dimensions in config are ignored. If func has a required cluster size set (see cudaFuncGetAttributes),*clusterSize will reflect the required cluster size.

By default this function will always return a value that’s portable on future hardware. A higher value may be returned if the kernel function allows non-portable cluster sizes.

This function will respect the compile time launch bounds.

See also

cudaFuncGetAttributes cudaOccupancyMaxPotentialClusterSize (C++ API), ::cuOccupancyMaxPotentialClusterSize

Note

Note that this function may also return error codes from previous, asynchronous launches.

Note

Note that this function may also return cudaErrorInitializationError, cudaErrorInsufficientDriver or cudaErrorNoDevice if this call tries to initialize internal CUDA RT state.

Note

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.

Note

The API can also be used with a kernel cudaKernel_t by querying the handle using cudaLibraryGetKernel() or cudaGetKernel and then passing it to the API by casting to void*. The symbol entryFuncAddr passed to cudaGetKernel should be a symbol that is registered with the same CUDA Runtime instance.

Note

Passing a symbol that belongs that belongs to a different runtime instance will result in undefined behavior. The only type that can be reliably passed to a different runtime instance is cudaKernel_t

Parameters

clusterSize – - Returned maximum cluster size that can be launched for the given kernel function and launch configuration
func – - Kernel function for which maximum cluster size is calculated
config – - Launch configuration for the given kernel function

Returns

cudaSuccess, cudaErrorInvalidDeviceFunction, cudaErrorInvalidValue, cudaErrorUnknown,