Provided by: nvidia-cuda-dev_9.1.85-3ubuntu1_amd64 
NAME
Execution Control -
Functions
__cudart_builtin__ cudaError_t cudaFuncGetAttributes (struct cudaFuncAttributes *attr,
const void *func)
Find out attributes for a given function.
__cudart_builtin__ cudaError_t cudaFuncSetAttribute (const void *func, enum
cudaFuncAttribute attr, int value)
Set attributes for a given function.
cudaError_t cudaFuncSetCacheConfig (const void *func, enum cudaFuncCache cacheConfig)
Sets the preferred cache configuration for a device function.
cudaError_t cudaFuncSetSharedMemConfig (const void *func, enum cudaSharedMemConfig config)
Sets the shared memory configuration for a device function.
__device__ __cudart_builtin__ void * cudaGetParameterBuffer (size_t alignment, size_t
size)
Obtains a parameter buffer.
__device__ __cudart_builtin__ void * cudaGetParameterBufferV2 (void *func, dim3
gridDimension, dim3 blockDimension, unsigned int sharedMemSize)
Launches a specified kernel.
cudaError_t cudaLaunchCooperativeKernel (const void *func, dim3 gridDim, dim3 blockDim,
void **args, size_t sharedMem, cudaStream_t stream)
Launches a device function where thread blocks can cooperate and synchronize as they
execute.
cudaError_t cudaLaunchCooperativeKernelMultiDevice (struct cudaLaunchParams
*launchParamsList, unsigned int numDevices, unsigned int flags=0)
Launches device functions on multiple devices where thread blocks can cooperate and
synchronize as they execute.
cudaError_t cudaLaunchKernel (const void *func, dim3 gridDim, dim3 blockDim, void **args,
size_t sharedMem, cudaStream_t stream)
Launches a device function.
cudaError_t cudaSetDoubleForDevice (double *d)
Converts a double argument to be executed on a device.
cudaError_t cudaSetDoubleForHost (double *d)
Converts a double argument after execution on a device.
Detailed Description
\brief execution control functions of the CUDA runtime API (cuda_runtime_api.h)
This section describes the execution control functions of the CUDA runtime application
programming interface.
Some functions have overloaded C++ API template versions documented separately in the C++
API Routines module.
Function Documentation
__cudart_builtin__ cudaError_t cudaFuncGetAttributes (struct cudaFuncAttributes * attr, const
void * func)
This function obtains the attributes of a function specified via func. func is a device
function symbol and 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. For templated functions, pass the function
symbol as follows: func_name<template_arg_0,...,template_arg_N>
Note that some function attributes such as maxThreadsPerBlock may vary based on the device
that is currently being used.
Parameters:
attr - Return pointer to function's attributes
func - Device function symbol
Returns:
cudaSuccess, cudaErrorInitializationError, cudaErrorInvalidDeviceFunction
Note:
Note that this function may also return error codes from previous, asynchronous
launches.
Use of a string naming a function as the func parameter was deprecated in CUDA 4.1 and
removed in CUDA 5.0.
See also:
cudaConfigureCall, cudaFuncSetCacheConfig (C API), cudaFuncGetAttributes (C++ API),
cudaLaunchKernel (C API), cudaSetDoubleForDevice, cudaSetDoubleForHost,
cudaSetupArgument (C API), cuFuncGetAttribute
__cudart_builtin__ cudaError_t cudaFuncSetAttribute (const void * func, enum cudaFuncAttribute
attr, int value)
This function sets 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 enumeration defined by attr is set to
the value defined by value If the specified function does not exist, then
cudaErrorInvalidDeviceFunction is returned. If the specified attribute cannot be written,
or if the value is incorrect, then cudaErrorInvalidValue is returned.
Valid values for attr are: cuFuncAttrMaxDynamicSharedMem - Maximum size of dynamic shared
memory per block cudaFuncAttributePreferredSharedMemoryCarveout - Preferred shared memory-
L1 cache split ratio
Parameters:
entry - Function to get attributes of
attr - Attribute to set
value - Value to set
Returns:
cudaSuccess, cudaErrorInitializationError, cudaErrorInvalidDeviceFunction,
cudaErrorInvalidValue
Note:
Note that this function may also return error codes from previous, asynchronous
launches.
cudaLaunchKernel (C++ API), cudaFuncSetCacheConfig (C++ API), cudaFuncGetAttributes (C
API), cudaSetDoubleForDevice, cudaSetDoubleForHost, cudaSetupArgument (C++ API)
cudaError_t cudaFuncSetCacheConfig (const void * 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 is a device function symbol and must be declared as a __global__ function. If the
specified function does not exist, then cudaErrorInvalidDeviceFunction is returned. For
templated functions, pass the function symbol as follows:
func_name<template_arg_0,...,template_arg_N>
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
• cudaFuncCachePreferEqual: prefer equal size L1 cache and shared memory
Parameters:
func - Device function symbol
cacheConfig - Requested cache configuration
Returns:
cudaSuccess, cudaErrorInitializationError, cudaErrorInvalidDeviceFunction
Note:
Note that this function may also return error codes from previous, asynchronous
launches.
Use of a string naming a function as the func parameter was deprecated in CUDA 4.1 and
removed in CUDA 5.0.
See also:
cudaConfigureCall, cudaFuncSetCacheConfig (C++ API), cudaFuncGetAttributes (C API),
cudaLaunchKernel (C API), cudaSetDoubleForDevice, cudaSetDoubleForHost,
cudaSetupArgument (C API), cudaThreadGetCacheConfig, cudaThreadSetCacheConfig,
cuFuncSetCacheConfig
cudaError_t cudaFuncSetSharedMemConfig (const void * func, enum cudaSharedMemConfig config)
On devices with configurable shared memory banks, this function will force all subsequent
launches of the specified device function to have the given shared memory bank size
configuration. On any given launch of the function, the shared memory configuration of the
device will be temporarily changed if needed to suit the function's preferred
configuration. Changes in shared memory configuration between subsequent launches of
functions, may introduce a device side synchronization point.
Any per-function setting of shared memory bank size set via cudaFuncSetSharedMemConfig
will override the device wide setting set by cudaDeviceSetSharedMemConfig.
Changing the shared memory bank size will not increase shared memory usage or affect
occupancy of kernels, but may have major effects on performance. Larger bank sizes will
allow for greater potential bandwidth to shared memory, but will change what kinds of
accesses to shared memory will result in bank conflicts.
This function will do nothing on devices with fixed shared memory bank size.
For templated functions, pass the function symbol as follows:
func_name<template_arg_0,...,template_arg_N>
The supported bank configurations are:
• cudaSharedMemBankSizeDefault: use the device's shared memory configuration when
launching this function.
• cudaSharedMemBankSizeFourByte: set shared memory bank width to be four bytes natively
when launching this function.
• cudaSharedMemBankSizeEightByte: set shared memory bank width to be eight bytes natively
when launching this function.
Parameters:
func - Device function symbol
config - Requested shared memory configuration
Returns:
cudaSuccess, cudaErrorInitializationError, cudaErrorInvalidDeviceFunction,
cudaErrorInvalidValue,
Note:
Note that this function may also return error codes from previous, asynchronous
launches.
Use of a string naming a function as the func parameter was deprecated in CUDA 4.1 and
removed in CUDA 5.0.
See also:
cudaConfigureCall, cudaDeviceSetSharedMemConfig, cudaDeviceGetSharedMemConfig,
cudaDeviceSetCacheConfig, cudaDeviceGetCacheConfig, cudaFuncSetCacheConfig,
cuFuncSetSharedMemConfig
__device__ __cudart_builtin__ void* cudaGetParameterBuffer (size_t alignment, size_t size)
Obtains a parameter buffer which can be filled with parameters for a kernel launch.
Parameters passed to cudaLaunchDevice must be allocated via this function.
This is a low level API and can only be accessed from Parallel Thread Execution (PTX).
CUDA user code should use <<< >>> to launch kernels.
Parameters:
alignment - Specifies alignment requirement of the parameter buffer
size - Specifies size requirement in bytes
Returns:
Returns pointer to the allocated parameterBuffer
Note:
Note that this function may also return error codes from previous, asynchronous
launches.
See also:
cudaLaunchDevice
__device__ __cudart_builtin__ void* cudaGetParameterBufferV2 (void * func, dim3 gridDimension,
dim3 blockDimension, unsigned int sharedMemSize)
Launches a specified kernel with the specified parameter buffer. A parameter buffer can be
obtained by calling cudaGetParameterBuffer().
This is a low level API and can only be accessed from Parallel Thread Execution (PTX).
CUDA user code should use <<< >>> to launch the kernels.
Parameters:
func - Pointer to the kernel to be launched
parameterBuffer - Holds the parameters to the launched kernel. parameterBuffer can be
NULL. (Optional)
gridDimension - Specifies grid dimensions
blockDimension - Specifies block dimensions
sharedMemSize - Specifies size of shared memory
stream - Specifies the stream to be used
Returns:
cudaSuccess, cudaErrorInvalidDevice, cudaErrorLaunchMaxDepthExceeded,
cudaErrorInvalidConfiguration, cudaErrorStartupFailure,
cudaErrorLaunchPendingCountExceeded, cudaErrorLaunchOutOfResources
Note:
Note that this function may also return error codes from previous, asynchronous
launches.
Please refer to Execution Configuration and Parameter Buffer Layout from the CUDA
Programming Guide for the detailed descriptions of launch configuration and parameter
layout respectively.
See also:
cudaGetParameterBuffer
cudaError_t cudaLaunchCooperativeKernel (const void * func, dim3 gridDim, dim3 blockDim, void
** args, size_t sharedMem, cudaStream_t stream)
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.
The device on which this kernel is invoked must have a non-zero value for the device
attribute cudaDevAttrCooperativeLaunch.
The total number of blocks launched cannot exceed the maximum number of blocks per
multiprocessor as returned by cudaOccupancyMaxActiveBlocksPerMultiprocessor (or
cudaOccupancyMaxActiveBlocksPerMultiprocessorWithFlags) times the number of
multiprocessors as specified by the device attribute cudaDevAttrMultiProcessorCount.
The kernel cannot make use of CUDA dynamic parallelism.
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.
For templated functions, pass the function symbol as follows:
func_name<template_arg_0,...,template_arg_N>
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.
Parameters:
func - Device function symbol
gridDim - Grid dimensions
blockDim - Block dimensions
args - Arguments
sharedMem - Shared memory
stream - Stream identifier
Returns:
cudaSuccess, cudaErrorInvalidDeviceFunction, cudaErrorInvalidConfiguration,
cudaErrorLaunchFailure, cudaErrorLaunchTimeout, cudaErrorLaunchOutOfResources,
cudaErrorCooperativeLaunchTooLarge, cudaErrorSharedObjectInitFailed
Note:
This function uses standard semantics.
Note that this function may also return error codes from previous, asynchronous
launches.
See also:
cudaLaunchCooperativeKernel (C++ API), cudaLaunchCooperativeKernelMultiDevice,
cuLaunchCooperativeKernel
cudaError_t cudaLaunchCooperativeKernelMultiDevice (struct cudaLaunchParams *
launchParamsList, unsigned int numDevices, unsigned int flags = 0)
Invokes kernels as specified in the launchParamsList array where each element of the array
specifies all the parameters required to perform a single kernel launch. These kernels can
cooperate and synchronize as they execute. The size of the array is specified by
numDevices.
No two kernels can be launched on the same device. All the devices targeted by this multi-
device launch must be identical. All devices must have a non-zero value for the device
attribute cudaDevAttrCooperativeLaunch.
The same kernel must be launched on all devices. Note that any __device__ or __constant__
variables are independently instantiated on every device. It is the application's
responsibility to ensure these variables are initialized and used appropriately.
The size of the grids as specified in blocks, the size of the blocks themselves and the
amount of shared memory used by each thread block must also match across all launched
kernels.
The streams used to launch these kernels must have been created via either
cudaStreamCreate or cudaStreamCreateWithPriority or cudaStreamCreateWithPriority. The NULL
stream or cudaStreamLegacy or cudaStreamPerThread cannot be used.
The total number of blocks launched per kernel cannot exceed the maximum number of blocks
per multiprocessor as returned by cudaOccupancyMaxActiveBlocksPerMultiprocessor (or
cudaOccupancyMaxActiveBlocksPerMultiprocessorWithFlags) times the number of
multiprocessors as specified by the device attribute cudaDevAttrMultiProcessorCount. Since
the total number of blocks launched per device has to match across all devices, the
maximum number of blocks that can be launched per device will be limited by the device
with the least number of multiprocessors.
The kernel cannot make use of CUDA dynamic parallelism.
The cudaLaunchParams structure is defined as:
struct cudaLaunchParams
{
void *func;
dim3 gridDim;
dim3 blockDim;
void **args;
size_t sharedMem;
cudaStream_t stream;
};
where:
• cudaLaunchParams::func specifies the kernel to be launched. This same functions must be
launched on all devices. For templated functions, pass the function symbol as follows:
func_name<template_arg_0,...,template_arg_N>
• cudaLaunchParams::gridDim specifies the width, height and depth of the grid in blocks.
This must match across all kernels launched.
• cudaLaunchParams::blockDim is the width, height and depth of each thread block. This
must match across all kernels launched.
• cudaLaunchParams::args specifies the arguments to the kernel. If the kernel has N
parameters then cudaLaunchParams::args should point to array of N pointers. Each
pointer, from cudaLaunchParams::args[0] to cudaLaunchParams::args[N - 1], point to the
region of memory from which the actual parameter will be copied.
• cudaLaunchParams::sharedMem is the dynamic shared-memory size per thread block in bytes.
This must match across all kernels launched.
• cudaLaunchParams::stream is the handle to the stream to perform the launch in. This
cannot be the NULL stream or cudaStreamLegacy or cudaStreamPerThread.
By default, the kernel won't begin execution on any GPU until all prior work in all the
specified streams has completed. This behavior can be overridden by specifying the flag
cudaCooperativeLaunchMultiDeviceNoPreSync. When this flag is specified, each kernel will
only wait for prior work in the stream corresponding to that GPU to complete before it
begins execution.
Similarly, by default, any subsequent work pushed in any of the specified streams will not
begin execution until the kernels on all GPUs have completed. This behavior can be
overridden by specifying the flag cudaCooperativeLaunchMultiDeviceNoPostSync. When this
flag is specified, any subsequent work pushed in any of the specified streams will only
wait for the kernel launched on the GPU corresponding to that stream to complete before it
begins execution.
Parameters:
launchParamsList - List of launch parameters, one per device
numDevices - Size of the launchParamsList array
flags - Flags to control launch behavior
Returns:
cudaSuccess, cudaErrorInvalidDeviceFunction, cudaErrorInvalidConfiguration,
cudaErrorLaunchFailure, cudaErrorLaunchTimeout, cudaErrorLaunchOutOfResources,
cudaErrorCooperativeLaunchTooLarge, cudaErrorSharedObjectInitFailed
Note:
This function uses standard semantics.
Note that this function may also return error codes from previous, asynchronous
launches.
See also:
cudaLaunchCooperativeKernel (C++ API), cudaLaunchCooperativeKernel,
cuLaunchCooperativeKernelMultiDevice
cudaError_t cudaLaunchKernel (const void * func, dim3 gridDim, dim3 blockDim, void ** args,
size_t sharedMem, cudaStream_t stream)
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.
For templated functions, pass the function symbol as follows:
func_name<template_arg_0,...,template_arg_N>
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.
Parameters:
func - Device function symbol
gridDim - Grid dimensions
blockDim - Block dimensions
args - Arguments
sharedMem - Shared memory
stream - Stream identifier
Returns:
cudaSuccess, cudaErrorInvalidDeviceFunction, cudaErrorInvalidConfiguration,
cudaErrorLaunchFailure, cudaErrorLaunchTimeout, cudaErrorLaunchOutOfResources,
cudaErrorSharedObjectInitFailed, cudaErrorInvalidPtx, cudaErrorNoKernelImageForDevice,
cudaErrorJitCompilerNotFound
Note:
This function uses standard semantics.
Note that this function may also return error codes from previous, asynchronous
launches.
See also:
cudaLaunchKernel (C++ API), cuLaunchKernel
cudaError_t cudaSetDoubleForDevice (double * d)
Parameters:
d - Double to convert
Deprecated
This function is deprecated as of CUDA 7.5
Converts the double value of d to an internal float representation if the device does not
support double arithmetic. If the device does natively support doubles, then this function
does nothing.
Returns:
cudaSuccess
Note:
Note that this function may also return error codes from previous, asynchronous
launches.
See also:
cudaLaunch (C API), cudaFuncSetCacheConfig (C API), cudaFuncGetAttributes (C API),
cudaSetDoubleForHost, cudaSetupArgument (C API)
cudaError_t cudaSetDoubleForHost (double * d)
Deprecated
This function is deprecated as of CUDA 7.5
Converts the double value of d from a potentially internal float representation if the
device does not support double arithmetic. If the device does natively support doubles,
then this function does nothing.
Parameters:
d - Double to convert
Returns:
cudaSuccess
Note:
Note that this function may also return error codes from previous, asynchronous
launches.
See also:
cudaLaunch (C API), cudaFuncSetCacheConfig (C API), cudaFuncGetAttributes (C API),
cudaSetDoubleForDevice, cudaSetupArgument (C API)
Author
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