tf
tf::IsPod
tf::SmallVectorBase
tf::SmallVectorStorage
tf::SmallVectorTemplateCommon
tf::SmallVectorTemplateBase
tf::SmallVectorTemplateBase< T, true >
tf::SmallVectorImpl
tf::SmallVectorStorage< T, 1 >
tf::SmallVectorStorage< T, 0 >
tf::SmallVector
tf::Graph
tf::Runtime
tf::TaskParams
tf::DefaultTaskParams
tf::Node
tf::UnboundedTaskQueue
tf::BoundedTaskQueue
tf::FlowBuilder
tf::Subflow
tf::Worker
tf::WorkerView
tf::Executor
tf::Task
tf::TaskView
tf::AsyncTask
tf::Semaphore
tf::Taskflow
tf::Future
tf::Segment
tf::Timeline
tf::ProfileData
tf::ObserverInterface
tf::ChromeObserver
tf::TFProfObserver
tf::TFProfManager
tf::DefaultClosureWrapper
tf::IsPartitioner
tf::PartitionerBase
tf::GuidedPartitioner
tf::DynamicPartitioner
tf::StaticPartitioner
tf::RandomPartitioner
tf::DeferredPipeflow
tf::Pipeflow
tf::Pipe
tf::Pipeline
tf::ScalablePipeline
tf::DataPipe
tf::DataPipeline
tf::cudaScopedDevice
tf::cudaSharedMemory
tf::cudaSharedMemory< int >
tf::cudaSharedMemory< unsigned int >
tf::cudaSharedMemory< char >
tf::cudaSharedMemory< unsigned char >
tf::cudaSharedMemory< short >
tf::cudaSharedMemory< unsigned short >
tf::cudaSharedMemory< long >
tf::cudaSharedMemory< unsigned long >
tf::cudaSharedMemory< bool >
tf::cudaSharedMemory< float >
tf::cudaSharedMemory< double >
tf::cudaDeviceAllocator
tf::cudaUSMAllocator
tf::cudaDeviceVector
tf::cudaStreamCreator
tf::cudaStreamDeleter
tf::cudaStream
tf::cudaEventCreator
tf::cudaEventDeleter
tf::cudaEvent
tf::cudaTask
tf::cudaFlow
tf::cudaFlowOptimizerBase
tf::cudaFlowSequentialOptimizer
tf::cudaFlowLinearOptimizer
tf::cudaFlowRoundRobinOptimizer
tf::cudaFlowCapturer
tf::cudaExecutionPolicy
tf::detail
tf::pt
int TaskType PLACEHOLDER = 0

placeholder task type

STATIC

static task type

SUBFLOW

dynamic (subflow) task type

CONDITION

condition task type

MODULE

module task type

ASYNC

asynchronous task type

UNDEFINED

undefined task type (for internal use only)

enumeration of all task types

int ObserverType TFPROF = 0 CHROME UNDEFINED

enumeration of all observer types

int PartitionerType STATIC

static partitioner type

DYNAMIC

dynamic partitioner type

enumeration of all partitioner types

int PipeType PARALLEL = 1

parallel type

SERIAL
= 2

serial type

enumeration of all pipe types

int cudaTaskType EMPTY = 0

empty task type

HOST

host task type

MEMSET

memory set task type

MEMCPY

memory copy task type

KERNEL

memory copy task type

SUBFLOW

subflow (child graph) task type

CAPTURE

capture task type

UNDEFINED

undefined task type

enumeration of all cudaTask types

NonblockingNotifierV2 using tf::DefaultNotifier = typedef NonblockingNotifierV2 DefaultNotifier
std::chrono::time_point< std::chrono::steady_clock > using tf::observer_stamp_t = typedef std::chrono::time_point<std::chrono::steady_clock> observer_stamp_t

default time point type of observers

GuidedPartitioner<> using tf::DefaultPartitioner = typedef GuidedPartitioner<> DefaultPartitioner

default partitioner set to tf::GuidedPartitioner

Guided partitioning algorithm can achieve stable and decent performance for most parallel algorithms.

cudaExecutionPolicy< 512, 7 > using tf::cudaDefaultExecutionPolicy = typedef cudaExecutionPolicy<512, 7> cudaDefaultExecutionPolicy

default execution policy

typename P
constexpr bool constexpr bool tf::is_task_params_v is_task_params_v = std::is_same_v<std::decay_t<P>, TaskParams> || std::is_same_v<std::decay_t<P>, DefaultTaskParams> || std::is_constructible_v<std::string, P>

determines if the given type is a task parameter type

Task parameters can be specified in one of the following types:

- tf::TaskParams: assign the struct of defined parameters

- tf::DefaultTaskParams: assign nothing

- std::string: assign a name to the task

constexpr std::array< TaskType, 6 > constexpr std::array<TaskType, 6> tf::TASK_TYPES TASK_TYPES = { TaskType::PLACEHOLDER, TaskType::STATIC, TaskType::SUBFLOW, TaskType::CONDITION, TaskType::MODULE, TaskType::ASYNC, }

array of all task types (used for iterating task types)

typename C
constexpr bool constexpr bool tf::is_subflow_task_v is_subflow_task_v = std::is_invocable_r_v<void, C, Subflow&> && !std::is_invocable_r_v<void, C, Runtime&>

determines if a callable is a dynamic task

A dynamic task is a callable object constructible from std::function<void(Subflow&)>.

typename C
constexpr bool constexpr bool tf::is_condition_task_v is_condition_task_v = (std::is_invocable_r_v<int, C> || std::is_invocable_r_v<int, C, Runtime&>) && !is_subflow_task_v<C>

determines if a callable is a condition task

A condition task is a callable object constructible from std::function<int()> or std::function<int(tf::Runtime&)>.

typename C
constexpr bool constexpr bool tf::is_multi_condition_task_v is_multi_condition_task_v = (std::is_invocable_r_v<SmallVector<int>, C> || std::is_invocable_r_v<SmallVector<int>, C, Runtime&>) && !is_subflow_task_v<C>

determines if a callable is a multi-condition task

A multi-condition task is a callable object constructible from std::function<tf::SmallVector<int>()> or std::function<tf::SmallVector<int>(tf::Runtime&)>.

typename C
constexpr bool constexpr bool tf::is_static_task_v is_static_task_v = (std::is_invocable_r_v<void, C> || std::is_invocable_r_v<void, C, Runtime&>) && !is_condition_task_v<C> && !is_multi_condition_task_v<C> && !is_subflow_task_v<C>

determines if a callable is a static task

A static task is a callable object constructible from std::function<void()> or std::function<void(tf::Runtime&)>.

typename P
constexpr bool constexpr bool tf::is_partitioner_v is_partitioner_v = std::is_base_of<IsPartitioner, P>::value

determines if a type is a partitioner

A partitioner is a derived type from tf::PartitionerBase.

typename T
unsigned N N
size_t static size_t tf::capacity_in_bytes (const SmallVector< T, N > &X) capacity_in_bytes const SmallVector< T, N > & X
typename... ArgsT ArgsT
TF_FORCE_INLINE Node * TF_FORCE_INLINE Node* tf::animate (ArgsT &&... args) animate ArgsT &&... args
TF_FORCE_INLINE void TF_FORCE_INLINE void tf::recycle (Node *ptr) recycle Node * ptr
const char * const char* tf::to_string (TaskType type) to_string TaskType type

convert a task type to a human-readable string

The name of each task type is the litte-case string of its characters.

    TaskType::PLACEHOLDER->"placeholder"
    TaskType::STATIC->"static"
    TaskType::SUBFLOW->"subflow"
    TaskType::CONDITION->"condition"
    TaskType::MODULE->"module"
    TaskType::ASYNC->"async"

std::ostream & std::ostream& tf::operator<< (std::ostream &os, const Task &task) operator<< std::ostream & os const Task & task

overload of ostream inserter operator for Task

typename I
std::enable_if_t< std::is_same_v< deref_t< I >, Semaphore >, void > * nullptr
bool bool tf::try_acquire (I first, I last) try_acquire I first I last

tries to acquire all semaphores in the specified range

I

iterator type

first

iterator to the beginning (inclusive)

last

iterator to the end (exclusive)

Tries to acquire all semaphores in the specified range.

`true` if all semaphores are acquired, otherwise `false`

typename... S S
std::enable_if_t< all_same_v< Semaphore, std::decay_t< S >... >, void > * nullptr
bool bool tf::try_acquire (S &&... semaphores) try_acquire S &&... semaphores

tries to acquire all semaphores

semaphores

semaphores to acquire

Tries to acquire all the semaphores.

`true` if all semaphores are acquired, otherwise `false`

typename I
std::enable_if_t< std::is_same_v< deref_t< I >, Semaphore >, void > * nullptr
void void tf::release (I first, I last) release I first I last

tries to acquire all semaphores in the specified range

I

iterator type

first

iterator to the beginning (inclusive)

last

iterator to the end (exclusive)

Releases all the semaphores in the given range.
typename... S S
std::enable_if_t< all_same_v< Semaphore, std::decay_t< S >... >, void > * nullptr
void void tf::release (S &&... semaphores) release S &&... semaphores

tries to acquire all semaphores

semaphores

semaphores to release

Releases all the semaphores.
const char * const char* tf::to_string (ObserverType type) to_string ObserverType type

convert an observer type to a human-readable string

typename Input
typename Output
typename C
auto auto tf::make_data_pipe (PipeType d, C &&callable) make_data_pipe PipeType d C && callable

function to construct a data pipe (tf::DataPipe)

Input

input data type

Output

output data type

C

callable type

tf::make_data_pipe is a helper function to create a data pipe (tf::DataPipe) in a data-parallel pipeline (tf::DataPipeline). The first argument specifies the direction of the data pipe, either tf::PipeType::SERIAL or tf::PipeType::PARALLEL, and the second argument is a callable to invoke by the pipeline scheduler. Input and output data types are specified via template parameters, which will always be decayed by the library to its original form for storage purpose. The callable must take the input data type in its first argument and returns a value of the output data type.

    tf::make_data_pipe<int,std::string>(
    tf::PipeType::SERIAL,
    [](int&input){
    returnstd::to_string(input+100);
    }
    );

The callable can additionally take a reference of tf::Pipeflow, which allows you to query the runtime information of a stage task, such as its line number and token number.

    tf::make_data_pipe<int,std::string>(
    tf::PipeType::SERIAL,
    [](int&input,tf::Pipeflow&pf){
    printf("token=%lu,line=%lu\n",pf.token(),pf.line());
    returnstd::to_string(input+100);
    }
    );

size_t size_t tf::cuda_get_num_devices () cuda_get_num_devices

queries the number of available devices

int int tf::cuda_get_device () cuda_get_device

gets the current device associated with the caller thread

void void tf::cuda_set_device (int id) cuda_set_device int id

switches to a given device context

void void tf::cuda_get_device_property (int i, cudaDeviceProp &p) cuda_get_device_property int i cudaDeviceProp & p

obtains the device property

cudaDeviceProp cudaDeviceProp tf::cuda_get_device_property (int i) cuda_get_device_property int i

obtains the device property

void void tf::cuda_dump_device_property (std::ostream &os, const cudaDeviceProp &p) cuda_dump_device_property std::ostream & os const cudaDeviceProp & p

dumps the device property

size_t size_t tf::cuda_get_device_max_threads_per_block (int d) cuda_get_device_max_threads_per_block int d

queries the maximum threads per block on a device

size_t size_t tf::cuda_get_device_max_x_dim_per_block (int d) cuda_get_device_max_x_dim_per_block int d

queries the maximum x-dimension per block on a device

size_t size_t tf::cuda_get_device_max_y_dim_per_block (int d) cuda_get_device_max_y_dim_per_block int d

queries the maximum y-dimension per block on a device

size_t size_t tf::cuda_get_device_max_z_dim_per_block (int d) cuda_get_device_max_z_dim_per_block int d

queries the maximum z-dimension per block on a device

size_t size_t tf::cuda_get_device_max_x_dim_per_grid (int d) cuda_get_device_max_x_dim_per_grid int d

queries the maximum x-dimension per grid on a device

size_t size_t tf::cuda_get_device_max_y_dim_per_grid (int d) cuda_get_device_max_y_dim_per_grid int d

queries the maximum y-dimension per grid on a device

size_t size_t tf::cuda_get_device_max_z_dim_per_grid (int d) cuda_get_device_max_z_dim_per_grid int d

queries the maximum z-dimension per grid on a device

size_t size_t tf::cuda_get_device_max_shm_per_block (int d) cuda_get_device_max_shm_per_block int d

queries the maximum shared memory size in bytes per block on a device

size_t size_t tf::cuda_get_device_warp_size (int d) cuda_get_device_warp_size int d

queries the warp size on a device

int int tf::cuda_get_device_compute_capability_major (int d) cuda_get_device_compute_capability_major int d

queries the major number of compute capability of a device

int int tf::cuda_get_device_compute_capability_minor (int d) cuda_get_device_compute_capability_minor int d

queries the minor number of compute capability of a device

bool bool tf::cuda_get_device_unified_addressing (int d) cuda_get_device_unified_addressing int d

queries if the device supports unified addressing

int int tf::cuda_get_driver_version () cuda_get_driver_version

queries the latest CUDA version (1000 * major + 10 * minor) supported by the driver

int int tf::cuda_get_runtime_version () cuda_get_runtime_version

queries the CUDA Runtime version (1000 * major + 10 * minor)

size_t size_t tf::cuda_get_free_mem (int d) cuda_get_free_mem int d

queries the free memory (expensive call)

size_t size_t tf::cuda_get_total_mem (int d) cuda_get_total_mem int d

queries the total available memory (expensive call)

typename T
T * T* tf::cuda_malloc_device (size_t N, int d) cuda_malloc_device size_t N int d

allocates memory on the given device for holding `N` elements of type `T`

The function calls `cudaMalloc` to allocate `N*sizeof(T)` bytes of memory on the given device `d` and returns a pointer to the starting address of the device memory.

typename T
T * T* tf::cuda_malloc_device (size_t N) cuda_malloc_device size_t N

allocates memory on the current device associated with the caller

The function calls malloc_device from the current device associated with the caller.

typename T
T * T* tf::cuda_malloc_shared (size_t N) cuda_malloc_shared size_t N

allocates shared memory for holding `N` elements of type `T`

The function calls `cudaMallocManaged` to allocate `N*sizeof(T)` bytes of memory and returns a pointer to the starting address of the shared memory.

typename T
void void tf::cuda_free (T *ptr, int d) cuda_free T * ptr int d

frees memory on the GPU device

T

pointer type

ptr

device pointer to memory to free

d

device context identifier

This methods call `cudaFree` to free the memory space pointed to by `ptr` using the given device context.
typename T
void void tf::cuda_free (T *ptr) cuda_free T * ptr

frees memory on the GPU device

T

pointer type

ptr

device pointer to memory to free

This methods call `cudaFree` to free the memory space pointed to by `ptr` using the current device context of the caller.
void void tf::cuda_memcpy_async (cudaStream_t stream, void *dst, const void *src, size_t count) cuda_memcpy_async cudaStream_t stream void * dst const void * src size_t count

copies data between host and device asynchronously through a stream

stream

stream identifier

dst

destination memory address

src

source memory address

count

size in bytes to copy

The method calls `cudaMemcpyAsync` with the given `stream` using `cudaMemcpyDefault` to infer the memory space of the source and the destination pointers. The memory areas may not overlap.
void void tf::cuda_memset_async (cudaStream_t stream, void *devPtr, int value, size_t count) cuda_memset_async cudaStream_t stream void * devPtr int value size_t count

initializes or sets GPU memory to the given value byte by byte

stream

stream identifier

devPtr

pointer to GPU memory

value

value to set for each byte of the specified memory

count

size in bytes to set

The method calls `cudaMemsetAsync` with the given `stream` to fill the first `count` bytes of the memory area pointed to by `devPtr` with the constant byte value `value`.
constexpr const char * constexpr const char* tf::to_string (cudaTaskType type) to_string cudaTaskType type

convert a cuda_task type to a human-readable string

std::ostream & std::ostream& tf::operator<< (std::ostream &os, const cudaTask &ct) operator<< std::ostream & os const cudaTask & ct

overload of ostream inserter operator for cudaTask

typename P
typename C
void void tf::cuda_single_task (P &&p, C c) cuda_single_task P && p C c

runs a callable asynchronously using one kernel thread

P

execution policy type

C

closure type

p

execution policy

c

closure to run by one kernel thread

The function launches a single kernel thread to run the given callable through the stream in the execution policy object.
typename P
typename I
typename C
void void tf::cuda_for_each (P &&p, I first, I last, C c) cuda_for_each P && p I first I last C c

performs asynchronous parallel iterations over a range of items

P

execution policy type

I

input iterator type

C

unary operator type

p

execution policy object

first

iterator to the beginning of the range

last

iterator to the end of the range

c

unary operator to apply to each dereferenced iterator

This function is equivalent to a parallel execution of the following loop on a GPU:

    for(autoitr=first;itr!=last;itr++){
    c(*itr);
    }

typename P
typename I
typename C
void void tf::cuda_for_each_index (P &&p, I first, I last, I inc, C c) cuda_for_each_index P && p I first I last I inc C c

performs asynchronous parallel iterations over an index-based range of items

P

execution policy type

I

input index type

C

unary operator type

p

execution policy object

first

index to the beginning of the range

last

index to the end of the range

inc

step size between successive iterations

c

unary operator to apply to each index

This function is equivalent to a parallel execution of the following loop on a GPU:

    //stepispositive[first,last)
    for(autoi=first;i<last;i+=step){
    c(i);
    }

    //stepisnegative[first,last)
    for(autoi=first;i>last;i+=step){
    c(i);
    }

typename C
__global__ void __global__ void tf::cuda_single_task (C callable) cuda_single_task C callable
typename P
typename I
typename O
typename C
void void tf::cuda_transform (P &&p, I first, I last, O output, C op) cuda_transform P && p I first I last O output C op

performs asynchronous parallel transforms over a range of items

P

execution policy type

I

input iterator type

O

output iterator type

C

unary operator type

p

execution policy

first

iterator to the beginning of the range

last

iterator to the end of the range

output

iterator to the beginning of the output range

op

unary operator to apply to transform each item

This method is equivalent to the parallel execution of the following loop on a GPU:

    while(first!=last){
    *output++=op(*first++);
    }

typename P
typename I1
typename I2
typename O
typename C
void void tf::cuda_transform (P &&p, I1 first1, I1 last1, I2 first2, O output, C op) cuda_transform P && p I1 first1 I1 last1 I2 first2 O output C op

performs asynchronous parallel transforms over two ranges of items

P

execution policy type

I1

first input iterator type

I2

second input iterator type

O

output iterator type

C

binary operator type

p

execution policy

first1

iterator to the beginning of the first range

last1

iterator to the end of the first range

first2

iterator to the beginning of the second range

output

iterator to the beginning of the output range

op

binary operator to apply to transform each pair of items

This method is equivalent to the parallel execution of the following loop on a GPU:

    while(first1!=last1){
    *output++=op(*first1++,*first2++);
    }

typename P
typename I
typename T
typename O
void void tf::cuda_reduce (P &&p, I first, I last, T *res, O op, void *buf) cuda_reduce P && p I first I last T * res O op void * buf

performs asynchronous parallel reduction over a range of items

P

execution policy type

I

input iterator type

T

value type

O

binary operator type

p

execution policy

first

iterator to the beginning of the range

last

iterator to the end of the range

res

pointer to the result

op

binary operator to apply to reduce elements

buf

pointer to the temporary buffer

This method is equivalent to the parallel execution of the following loop on a GPU:

    while(first!=last){
    *result=op(*result,*first++);
    }

typename P
typename I
typename T
typename O
void void tf::cuda_uninitialized_reduce (P &&p, I first, I last, T *res, O op, void *buf) cuda_uninitialized_reduce P && p I first I last T * res O op void * buf

performs asynchronous parallel reduction over a range of items without an initial value

P

execution policy type

I

input iterator type

T

value type

O

binary operator type

p

execution policy

first

iterator to the beginning of the range

last

iterator to the end of the range

res

pointer to the result

op

binary operator to apply to reduce elements

buf

pointer to the temporary buffer

This method is equivalent to the parallel execution of the following loop on a GPU:

    *result=*first++;//noinitialvaluespartitipcateintheloop
    while(first!=last){
    *result=op(*result,*first++);
    }

typename P
typename I
typename T
typename O
typename U
void void tf::cuda_transform_reduce (P &&p, I first, I last, T *res, O bop, U uop, void *buf) cuda_transform_reduce P && p I first I last T * res O bop U uop void * buf

performs asynchronous parallel reduction over a range of transformed items without an initial value

P

execution policy type

I

input iterator type

T

value type

O

binary operator type

U

unary operator type

p

execution policy

first

iterator to the beginning of the range

last

iterator to the end of the range

res

pointer to the result

bop

binary operator to apply to reduce elements

uop

unary operator to apply to transform elements

buf

pointer to the temporary buffer

This method is equivalent to the parallel execution of the following loop on a GPU:

    while(first!=last){
    *result=bop(*result,uop(*first++));
    }

typename P
typename I
typename T
typename O
typename U
void void tf::cuda_uninitialized_transform_reduce (P &&p, I first, I last, T *res, O bop, U uop, void *buf) cuda_uninitialized_transform_reduce P && p I first I last T * res O bop U uop void * buf

performs asynchronous parallel reduction over a range of transformed items with an initial value

P

execution policy type

I

input iterator type

T

value type

O

binary operator type

U

unary operator type

p

execution policy

first

iterator to the beginning of the range

last

iterator to the end of the range

res

pointer to the result

bop

binary operator to apply to reduce elements

uop

unary operator to apply to transform elements

buf

pointer to the temporary buffer

This method is equivalent to the parallel execution of the following loop on a GPU:

    *result=uop(*first++);//noinitialvaluespartitipcateintheloop
    while(first!=last){
    *result=bop(*result,uop(*first++));
    }

typename P
typename I
typename O
typename C
void void tf::cuda_inclusive_scan (P &&p, I first, I last, O output, C op, void *buf) cuda_inclusive_scan P && p I first I last O output C op void * buf

performs asynchronous inclusive scan over a range of items

P

execution policy type

I

input iterator

O

output iterator

C

binary operator type

p

execution policy

first

iterator to the beginning of the input range

last

iterator to the end of the input range

output

iterator to the beginning of the output range

op

binary operator to apply to scan

buf

pointer to the temporary buffer

typename P
typename I
typename O
typename C
typename U
void void tf::cuda_transform_inclusive_scan (P &&p, I first, I last, O output, C bop, U uop, void *buf) cuda_transform_inclusive_scan P && p I first I last O output C bop U uop void * buf

performs asynchronous inclusive scan over a range of transformed items

P

execution policy type

I

input iterator

O

output iterator

C

binary operator type

U

unary operator type

p

execution policy

first

iterator to the beginning of the input range

last

iterator to the end of the input range

output

iterator to the beginning of the output range

bop

binary operator to apply to scan

uop

unary operator to apply to transform each item before scan

buf

pointer to the temporary buffer

typename P
typename I
typename O
typename C
void void tf::cuda_exclusive_scan (P &&p, I first, I last, O output, C op, void *buf) cuda_exclusive_scan P && p I first I last O output C op void * buf

performs asynchronous exclusive scan over a range of items

P

execution policy type

I

input iterator

O

output iterator

C

binary operator type

p

execution policy

first

iterator to the beginning of the input range

last

iterator to the end of the input range

output

iterator to the beginning of the output range

op

binary operator to apply to scan

buf

pointer to the temporary buffer

typename P
typename I
typename O
typename C
typename U
void void tf::cuda_transform_exclusive_scan (P &&p, I first, I last, O output, C bop, U uop, void *buf) cuda_transform_exclusive_scan P && p I first I last O output C bop U uop void * buf

performs asynchronous exclusive scan over a range of items

P

execution policy type

I

input iterator

O

output iterator

C

binary operator type

U

unary operator type

p

execution policy

first

iterator to the beginning of the input range

last

iterator to the end of the input range

output

iterator to the beginning of the output range

bop

binary operator to apply to scan

uop

unary operator to apply to transform each item before scan

buf

pointer to the temporary buffer

typename P
typename a_keys_it
typename a_vals_it
typename b_keys_it
typename b_vals_it
typename c_keys_it
typename c_vals_it
typename C
void void tf::cuda_merge_by_key (P &&p, a_keys_it a_keys_first, a_keys_it a_keys_last, a_vals_it a_vals_first, b_keys_it b_keys_first, b_keys_it b_keys_last, b_vals_it b_vals_first, c_keys_it c_keys_first, c_vals_it c_vals_first, C comp, void *buf) cuda_merge_by_key P && p a_keys_it a_keys_first a_keys_it a_keys_last a_vals_it a_vals_first b_keys_it b_keys_first b_keys_it b_keys_last b_vals_it b_vals_first c_keys_it c_keys_first c_vals_it c_vals_first C comp void * buf

performs asynchronous key-value merge over a range of keys and values

P

execution policy type

a_keys_it

first key iterator type

a_vals_it

first value iterator type

b_keys_it

second key iterator type

b_vals_it

second value iterator type

c_keys_it

output key iterator type

c_vals_it

output value iterator type

C

comparator type

p

execution policy

a_keys_first

iterator to the beginning of the first key range

a_keys_last

iterator to the end of the first key range

a_vals_first

iterator to the beginning of the first value range

b_keys_first

iterator to the beginning of the second key range

b_keys_last

iterator to the end of the second key range

b_vals_first

iterator to the beginning of the second value range

c_keys_first

iterator to the beginning of the output key range

c_vals_first

iterator to the beginning of the output value range

comp

comparator

buf

pointer to the temporary buffer

Performs a key-value merge that copies elements from `[a_keys_first, a_keys_last)` and `[b_keys_first, b_keys_last)` into a single range, `[c_keys_first, c_keys_last + (a_keys_last - a_keys_first) + (b_keys_last - b_keys_first))` such that the resulting range is in ascending key order.

At the same time, the merge copies elements from the two associated ranges `[a_vals_first + (a_keys_last - a_keys_first))` and `[b_vals_first + (b_keys_last - b_keys_first))` into a single range, `[c_vals_first, c_vals_first + (a_keys_last - a_keys_first) + (b_keys_last - b_keys_first))` such that the resulting range is in ascending order implied by each input element's associated key.

For example, assume:

- `a_keys` = {1, 8};

- `a_vals` = {2, 1};

- `b_keys` = {3, 7};

- `b_vals` = {3, 4};

After the merge, we have:

- `c_keys` = {1, 3, 7, 8}

- `c_vals` = {2, 3, 4, 1}

typename P
typename a_keys_it
typename b_keys_it
typename c_keys_it
typename C
void void tf::cuda_merge (P &&p, a_keys_it a_keys_first, a_keys_it a_keys_last, b_keys_it b_keys_first, b_keys_it b_keys_last, c_keys_it c_keys_first, C comp, void *buf) cuda_merge P && p a_keys_it a_keys_first a_keys_it a_keys_last b_keys_it b_keys_first b_keys_it b_keys_last c_keys_it c_keys_first C comp void * buf

performs asynchronous key-only merge over a range of keys

P

execution policy type

a_keys_it

first key iterator type

b_keys_it

second key iterator type

c_keys_it

output key iterator type

C

comparator type

p

execution policy

a_keys_first

iterator to the beginning of the first key range

a_keys_last

iterator to the end of the first key range

b_keys_first

iterator to the beginning of the second key range

b_keys_last

iterator to the end of the second key range

c_keys_first

iterator to the beginning of the output key range

comp

comparator

buf

pointer to the temporary buffer

This function is equivalent to tf::cuda_merge_by_key without values.
typename P
typename K
typename V cudaEmpty
unsigned unsigned tf::cuda_sort_buffer_size (unsigned count) cuda_sort_buffer_size unsigned count

queries the buffer size in bytes needed to call sort kernels for the given number of elements

P

execution policy type

K

key type

V

value type (default tf::cudaEmpty)

count

number of keys/values to sort

The function is used to allocate a buffer for calling tf::cuda_sort.
typename P
typename K_it
typename V_it
typename C
void void tf::cuda_sort_by_key (P &&p, K_it k_first, K_it k_last, V_it v_first, C comp, void *buf) cuda_sort_by_key P && p K_it k_first K_it k_last V_it v_first C comp void * buf

performs asynchronous key-value sort on a range of items

P

execution policy type

K_it

key iterator type

V_it

value iterator type

C

comparator type

p

execution policy

k_first

iterator to the beginning of the key range

k_last

iterator to the end of the key range

v_first

iterator to the beginning of the value range

comp

binary comparator

buf

pointer to the temporary buffer

Sorts key-value elements in `[k_first, k_last)` and `[v_first, v_first + (k_last - k_first))` into ascending key order using the given comparator `comp`. If `i` and `j` are any two valid iterators in `[k_first, k_last)` such that `i` precedes `j`, and `p` and `q` are iterators in `[v_first, v_first + (k_last - k_first))` corresponding to `i` and `j` respectively, then `comp(*j, *i)` evaluates to `false`.

For example, assume:

- `keys` are `{1, 4, 2, 8, 5, 7}`

- `values` are `{'a', 'b', 'c', 'd', 'e', 'f'}`

After sort:

- `keys` are `{1, 2, 4, 5, 7, 8}`

- `values` are `{'a', 'c', 'b', 'e', 'f', 'd'}`

typename P
typename K_it
typename C
void void tf::cuda_sort (P &&p, K_it k_first, K_it k_last, C comp, void *buf) cuda_sort P && p K_it k_first K_it k_last C comp void * buf

performs asynchronous key-only sort on a range of items

P

execution policy type

K_it

key iterator type

C

comparator type

p

execution policy

k_first

iterator to the beginning of the key range

k_last

iterator to the end of the key range

comp

binary comparator

buf

pointer to the temporary buffer

This method is equivalent to tf::cuda_sort_by_key without values.
typename P
typename I
typename U
void void tf::cuda_find_if (P &&p, I first, I last, unsigned *idx, U op) cuda_find_if P && p I first I last unsigned * idx U op

finds the index of the first element that satisfies the given criteria

P

execution policy type

I

input iterator type

U

unary operator type

p

execution policy

first

iterator to the beginning of the range

last

iterator to the end of the range

idx

pointer to the index of the found element

op

unary operator which returns `true` for the required element

The function launches kernels asynchronously to find the index `idx` of the first element in the range `[first, last)` such that `op(*(first+idx))` is true. This is equivalent to the parallel execution of the following loop:

    unsignedidx=0;
    for(;first!=last;++first,++idx){
    if(p(*first)){
    returnidx;
    }
    }
    returnidx;

typename P
typename I
typename O
void void tf::cuda_min_element (P &&p, I first, I last, unsigned *idx, O op, void *buf) cuda_min_element P && p I first I last unsigned * idx O op void * buf

finds the index of the minimum element in a range

P

execution policy type

I

input iterator type

O

comparator type

p

execution policy object

first

iterator to the beginning of the range

last

iterator to the end of the range

idx

solution index of the minimum element

op

comparison function object

buf

pointer to the buffer

The function launches kernels asynchronously to find the smallest element in the range `[first, last)` using the given comparator `op`. You need to provide a buffer that holds at least tf::cuda_min_element_bufsz bytes for internal use. The function is equivalent to a parallel execution of the following loop:

    if(first==last){
    return0;
    }
    autosmallest=first;
    for(++first;first!=last;++first){
    if(op(*first,*smallest)){
    smallest=first;
    }
    }
    returnstd::distance(first,smallest);

typename P
typename I
typename O
void void tf::cuda_max_element (P &&p, I first, I last, unsigned *idx, O op, void *buf) cuda_max_element P && p I first I last unsigned * idx O op void * buf

finds the index of the maximum element in a range

P

execution policy type

I

input iterator type

O

comparator type

p

execution policy object

first

iterator to the beginning of the range

last

iterator to the end of the range

idx

solution index of the maximum element

op

comparison function object

buf

pointer to the buffer

The function launches kernels asynchronously to find the largest element in the range `[first, last)` using the given comparator `op`. You need to provide a buffer that holds at least tf::cuda_max_element_bufsz bytes for internal use. The function is equivalent to a parallel execution of the following loop:

    if(first==last){
    return0;
    }
    autolargest=first;
    for(++first;first!=last;++first){
    if(op(*largest,*first)){
    largest=first;
    }
    }
    returnstd::distance(first,largest);

constexpr const char * constexpr const char* tf::version () version

queries the version information in a string format `major.minor.patch`

Release notes are available here: https://taskflow.github.io/taskflow/Releases.html

taskflow namespace