# tf::cudaFlowCapturer class
#include <taskflow/cuda/cuda_capturer.hpp>

class to create a cudaFlow graph using stream capture

The usage of tf::cudaFlowCapturer is similar to tf::cudaFlow, except users can call the method tf::cudaFlowCapturer::on to capture a sequence of asynchronous CUDA operations through the given stream. The following example creates a CUDA graph that captures two kernel tasks, `task_1` and `task_2`, where `task_1` runs before `task_2`.

    taskflow.emplace([](tf::cudaFlowCapturer& capturer){

      // capture my_kernel_1 through the given stream managed by the capturer
      auto task_1 = capturer.on([&](cudaStream_t stream){
        my_kernel_1<<<grid_1, block_1, shm_size_1, stream>>>(my_parameters_1);
      });

      // capture my_kernel_2 through the given stream managed by the capturer
      auto task_2 = capturer.on([&](cudaStream_t stream){
        my_kernel_2<<<grid_2, block_2, shm_size_2, stream>>>(my_parameters_2);
      });

      task_1.precede(task_2);
    });

Similar to tf::cudaFlow, a cudaFlowCapturer is a task (tf::Task) created from tf::Taskflow and will be run by one worker thread in the executor. That is, the callable that describes a cudaFlowCapturer will be executed sequentially. Inside a cudaFlow capturer task, different GPU tasks (tf::cudaTask) may run in parallel depending on the selected optimization algorithm. By default, we use tf::cudaFlowRoundRobinOptimizer to transform a user-level graph into a native CUDA graph.

Please refer to GPU Tasking (cudaFlowCapturer) for details.

## Constructors, destructors, conversion operators

cudaFlowCapturer() defaulted
  constructs a standalone cudaFlowCapturer
~cudaFlowCapturer() defaulted
  destructs the cudaFlowCapturer
cudaFlowCapturer( cudaFlowCapturer&&) defaulted
  default move constructor

## Public functions

auto operator=( cudaFlowCapturer&&) -> cudaFlowCapturer& defaulted
  default move assignment operator
auto empty() const -> bool
  queries the emptiness of the graph
auto num_tasks() const -> size_t
  queries the number of tasks
void clear()
  clear this cudaFlow capturer
void dump( std::ostream& os) const
  dumps the cudaFlow graph into a DOT format through an output stream
void dump_native_graph( std::ostream& os) const
  dumps the native captured graph into a DOT format through an output stream
template<typename C, std::enable_if_t<std::is_invocable_r_v<void, C, cudaStream_t>, void>* = nullptr>
auto on(C&& callable) -> cudaTask
  captures a sequential CUDA operations from the given callable
template<typename C, std::enable_if_t<std::is_invocable_r_v<void, C, cudaStream_t>, void>* = nullptr>
void on( cudaTask task, C&& callable)
  updates a capture task to another sequential CUDA operations
auto noop() -> cudaTask
  captures a no-operation task
void noop( cudaTask task)
  updates a task to a no-operation task
auto memcpy(void* dst, const void* src, size_t count) -> cudaTask
  copies data between host and device asynchronously through a stream
void memcpy( cudaTask task, void* dst, const void* src, size_t count)
  updates a capture task to a memcpy operation
template<typename T, std::enable_if_t<!std::is_same_v<T, void>, void>* = nullptr>
auto copy(T* tgt, const T* src, size_t num) -> cudaTask
  captures a copy task of typed data
template<typename T, std::enable_if_t<!std::is_same_v<T, void>, void>* = nullptr>
void copy( cudaTask task, T* tgt, const T* src, size_t num)
  updates a capture task to a copy operation
auto memset(void* ptr, int v, size_t n) -> cudaTask
  initializes or sets GPU memory to the given value byte by byte
void memset( cudaTask task, void* ptr, int value, size_t n)
  updates a capture task to a memset operation
template<typename F, typename... ArgsT>
auto kernel(dim3 g, dim3 b, size_t s, F f, ArgsT && ... args) -> cudaTask
  captures a kernel
template<typename F, typename... ArgsT>
void kernel( cudaTask task, dim3 g, dim3 b, size_t s, F f, ArgsT && ... args)
  updates a capture task to a kernel operation
template<typename C>
auto single_task(C c) -> cudaTask
  capturers a kernel to runs the given callable with only one thread
template<typename C>
void single_task( cudaTask task, C c)
  updates a capture task to a single-threaded kernel
template<typename I, typename C>
auto for_each(I first, I last, C callable) -> cudaTask
  captures a kernel that applies a callable to each dereferenced element of the data array
template<typename I, typename C>
void for_each( cudaTask task, I first, I last, C callable)
  updates a capture task to a for-each kernel task
template<typename I, typename C>
auto for_each_index(I first, I last, I step, C callable) -> cudaTask
  captures a kernel that applies a callable to each index in the range with the step size
template<typename I, typename C>
void for_each_index( cudaTask task, I first, I last, I step, C callable)
  updates a capture task to a for-each-index kernel task
template<typename I, typename O, typename C>
auto transform(I first, I last, O output, C op) -> cudaTask
  captures a kernel that transforms an input range to an output range
template<typename I, typename O, typename C>
void transform( cudaTask task, I first, I last, O output, C op)
  updates a capture task to a transform kernel task
template<typename I1, typename I2, typename O, typename C>
auto transform(I1 first1, I1 last1, I2 first2, O output, C op) -> cudaTask
  captures a kernel that transforms two input ranges to an output range
template<typename I1, typename I2, typename O, typename C>
void transform( cudaTask task, I1 first1, I1 last1, I2 first2, O output, C op)
  updates a capture task to a transform kernel task
template<typename OPT, typename... ArgsT>
auto make_optimizer(ArgsT && ... args) -> OPT&
  selects a different optimization algorithm
auto capture() -> cudaGraph_t
  captures the cudaFlow and turns it into a CUDA Graph
void run(cudaStream_t stream)
  offloads the cudaFlowCapturer onto a GPU asynchronously via a stream
auto native_graph() -> cudaGraph_t
  acquires a reference to the underlying CUDA graph
auto native_executable() -> cudaGraphExec_t
  acquires a reference to the underlying CUDA graph executable

## Function documentation

### tf::cudaFlowCapturer:: cudaFlowCapturer() defaulted

constructs a standalone cudaFlowCapturer

A standalone cudaFlow capturer does not go through any taskflow and can be run by the caller thread using tf::cudaFlowCapturer::run.

###
template<typename C, std::enable_if_t<std::is_invocable_r_v<void, C, cudaStream_t>, void>* = nullptr>
cudaTask tf::cudaFlowCapturer:: on(C&& callable)

captures a sequential CUDA operations from the given callable

Template parameters
C | callable type constructible with `std::function<void(cudaStream_t)>`
Parameters
callable | a callable to capture CUDA operations with the stream

This methods applies a stream created by the flow to capture a sequence of CUDA operations defined in the callable.

###
template<typename C, std::enable_if_t<std::is_invocable_r_v<void, C, cudaStream_t>, void>* = nullptr>
void tf::cudaFlowCapturer:: on( cudaTask task, C&& callable)

updates a capture task to another sequential CUDA operations

The method is similar to cudaFlowCapturer::on but operates on an existing task.

### cudaTask tf::cudaFlowCapturer:: noop()

captures a no-operation task

Returns | a tf::cudaTask handle

An empty node performs no operation during execution, but can be used for transitive ordering. For example, a phased execution graph with 2 groups of `n` nodes with a barrier between them can be represented using an empty node and `2*n` dependency edges, rather than no empty node and `n^2` dependency edges.

### void tf::cudaFlowCapturer:: noop( cudaTask task)

updates a task to a no-operation task

The method is similar to tf::cudaFlowCapturer::noop but operates on an existing task.

### cudaTask tf::cudaFlowCapturer:: memcpy(void* dst, const void* src, size_t count)

copies data between host and device asynchronously through a stream

Parameters
dst | destination memory address
src | source memory address
count | size in bytes to copy

The method captures a `cudaMemcpyAsync` operation through an internal stream.

### void tf::cudaFlowCapturer:: memcpy( cudaTask task, void* dst, const void* src, size_t count)

updates a capture task to a memcpy operation

The method is similar to cudaFlowCapturer::memcpy but operates on an existing task.

###
template<typename T, std::enable_if_t<!std::is_same_v<T, void>, void>* = nullptr>
cudaTask tf::cudaFlowCapturer:: copy(T* tgt, const T* src, size_t num)

captures a copy task of typed data

Template parameters
T | element type (non-void)
Parameters
tgt | pointer to the target memory block
src | pointer to the source memory block
num | number of elements to copy
Returns | cudaTask handle

A copy task transfers `num*sizeof(T)` bytes of data from a source location to a target location. Direction can be arbitrary among CPUs and GPUs.

###
template<typename T, std::enable_if_t<!std::is_same_v<T, void>, void>* = nullptr>
void tf::cudaFlowCapturer:: copy( cudaTask task, T* tgt, const T* src, size_t num)

updates a capture task to a copy operation

The method is similar to cudaFlowCapturer::copy but operates on an existing task.

### cudaTask tf::cudaFlowCapturer:: memset(void* ptr, int v, size_t n)

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

Parameters
ptr | pointer to GPU memory
v | value to set for each byte of the specified memory
n | size in bytes to set

The method captures a `cudaMemsetAsync` operation through an internal stream to fill the first `count` bytes of the memory area pointed to by `devPtr` with the constant byte value `value`.

### void tf::cudaFlowCapturer:: memset( cudaTask task, void* ptr, int value, size_t n)

updates a capture task to a memset operation

The method is similar to cudaFlowCapturer::memset but operates on an existing task.

###
template<typename F, typename... ArgsT>
cudaTask tf::cudaFlowCapturer:: kernel(dim3 g, dim3 b, size_t s, F f, ArgsT && ... args)

captures a kernel

Template parameters
F | kernel function type
ArgsT | kernel function parameters type
Parameters
g | configured grid
b | configured block
s | configured shared memory size in bytes
f | kernel function
args | arguments to forward to the kernel function by copy
Returns | cudaTask handle

###
template<typename F, typename... ArgsT>
void tf::cudaFlowCapturer:: kernel( cudaTask task, dim3 g, dim3 b, size_t s, F f, ArgsT && ... args)

updates a capture task to a kernel operation

The method is similar to cudaFlowCapturer::kernel but operates on an existing task.

###
template<typename C>
cudaTask tf::cudaFlowCapturer:: single_task(C c)

capturers a kernel to runs the given callable with only one thread

Template parameters
C | callable type
Parameters
c | callable to run by a single kernel thread

###
template<typename C>
void tf::cudaFlowCapturer:: single_task( cudaTask task, C c)

updates a capture task to a single-threaded kernel

This method is similar to cudaFlowCapturer::single_task but operates on an existing task.

###
template<typename I, typename C>
cudaTask tf::cudaFlowCapturer:: for_each(I first, I last, C callable)

captures a kernel that applies a callable to each dereferenced element of the data array

Template parameters
I | iterator type
C | callable type
Parameters
first | iterator to the beginning
last | iterator to the end
callable | a callable object to apply to the dereferenced iterator
Returns | cudaTask handle

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

    for(auto itr = first; itr != last; i++) {
      callable(*itr);
    }

###
template<typename I, typename C>
void tf::cudaFlowCapturer:: for_each( cudaTask task, I first, I last, C callable)

updates a capture task to a for-each kernel task

This method is similar to cudaFlowCapturer::for_each but operates on an existing task.

###
template<typename I, typename C>
cudaTask tf::cudaFlowCapturer:: for_each_index(I first, I last, I step, C callable)

captures a kernel that applies a callable to each index in the range with the step size

Template parameters
I | index type
C | callable type
Parameters
first | beginning index
last | last index
step | step size
callable | the callable to apply to each element in the data array
Returns | cudaTask handle

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

    // step is positive [first, last)
    for(auto i=first; i<last; i+=step) {
      callable(i);
    }

    // step is negative [first, last)
    for(auto i=first; i>last; i+=step) {
      callable(i);
    }

###
template<typename I, typename C>
void tf::cudaFlowCapturer:: for_each_index( cudaTask task, I first, I last, I step, C callable)

updates a capture task to a for-each-index kernel task

This method is similar to cudaFlowCapturer::for_each_index but operates on an existing task.

###
template<typename I, typename O, typename C>
cudaTask tf::cudaFlowCapturer:: transform(I first, I last, O output, C op)

captures a kernel that transforms an input range to an output range

Template parameters
I | input iterator type
O | output iterator type
C | unary operator type
Parameters
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 | unary operator to apply to transform each item in the range
Returns | cudaTask handle

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

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

###
template<typename I, typename O, typename C>
void tf::cudaFlowCapturer:: transform( cudaTask task, I first, I last, O output, C op)

updates a capture task to a transform kernel task

This method is similar to cudaFlowCapturer::transform but operates on an existing task.

###
template<typename I1, typename I2, typename O, typename C>
cudaTask tf::cudaFlowCapturer:: transform(I1 first1, I1 last1, I2 first2, O output, C op)

captures a kernel that transforms two input ranges to an output range

Template parameters
I1 | first input iterator type
I2 | second input iterator type
O | output iterator type
C | unary operator type
Parameters
first1 | iterator to the beginning of the input range
last1 | iterator to the end of the input range
first2 | iterato
output | iterator to the beginning of the output range
op | binary operator to apply to transform each pair of items in the two input ranges
Returns | cudaTask handle

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

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

###
template<typename I1, typename I2, typename O, typename C>
void tf::cudaFlowCapturer:: transform( cudaTask task, I1 first1, I1 last1, I2 first2, O output, C op)

updates a capture task to a transform kernel task

This method is similar to cudaFlowCapturer::transform but operates on an existing task.

###
template<typename OPT, typename... ArgsT>
OPT& tf::cudaFlowCapturer:: make_optimizer(ArgsT && ... args)

selects a different optimization algorithm

Template parameters
OPT | optimizer type
ArgsT | arguments types
Parameters
args | arguments to forward to construct the optimizer
Returns | a reference to the optimizer

We currently supports the following optimization algorithms to capture a user-described cudaFlow:

- tf::cudaFlowSequentialOptimizer
- tf::cudaFlowRoundRobinOptimizer
- tf::cudaFlowLinearOptimizer

By default, tf::cudaFlowCapturer uses the round-robin optimization algorithm with four streams to transform a user-level graph into a native CUDA graph.

### void tf::cudaFlowCapturer:: run(cudaStream_t stream)

offloads the cudaFlowCapturer onto a GPU asynchronously via a stream

Parameters
stream | stream for performing this operation

Offloads the present cudaFlowCapturer onto a GPU asynchronously via the given stream.

An offloaded cudaFlowCapturer forces the underlying graph to be instantiated. After the instantiation, you should not modify the graph topology but update node parameters.

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