1024 lines
28 KiB
C++
1024 lines
28 KiB
C++
#pragma once
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#include "../taskflow.hpp"
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#include "cuda_task.hpp"
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#include "cuda_capturer.hpp"
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/**
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@file taskflow/cuda/cudaflow.hpp
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@brief cudaFlow include file
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*/
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namespace tf {
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// ----------------------------------------------------------------------------
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// class definition: cudaFlow
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// ----------------------------------------------------------------------------
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/**
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@class cudaFlow
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@brief class to create a %cudaFlow task dependency graph
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A %cudaFlow is a high-level interface over CUDA Graph to perform GPU operations
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using the task dependency graph model.
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The class provides a set of methods for creating and launch different tasks
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on one or multiple CUDA devices,
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for instance, kernel tasks, data transfer tasks, and memory operation tasks.
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The following example creates a %cudaFlow of two kernel tasks, @c task1 and
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@c task2, where @c task1 runs before @c task2.
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@code{.cpp}
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tf::Taskflow taskflow;
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tf::Executor executor;
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taskflow.emplace([&](tf::cudaFlow& cf){
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// create two kernel tasks
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tf::cudaTask task1 = cf.kernel(grid1, block1, shm_size1, kernel1, args1);
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tf::cudaTask task2 = cf.kernel(grid2, block2, shm_size2, kernel2, args2);
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// kernel1 runs before kernel2
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task1.precede(task2);
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});
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executor.run(taskflow).wait();
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@endcode
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A %cudaFlow is a task (tf::Task) created from tf::Taskflow
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and will be run by @em one worker thread in the executor.
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That is, the callable that describes a %cudaFlow
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will be executed sequentially.
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Inside a %cudaFlow task, different GPU tasks (tf::cudaTask) may run
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in parallel scheduled by the CUDA runtime.
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Please refer to @ref GPUTaskingcudaFlow for details.
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*/
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class cudaFlow {
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public:
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/**
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@brief constructs a %cudaFlow
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*/
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cudaFlow();
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/**
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@brief destroys the %cudaFlow and its associated native CUDA graph
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and executable graph
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*/
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~cudaFlow() = default;
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/**
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@brief default move constructor
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*/
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cudaFlow(cudaFlow&&) = default;
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/**
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@brief default move assignment operator
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*/
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cudaFlow& operator = (cudaFlow&&) = default;
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/**
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@brief queries the emptiness of the graph
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*/
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bool empty() const;
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/**
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@brief queries the number of tasks
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*/
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size_t num_tasks() const;
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/**
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@brief clears the %cudaFlow object
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*/
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void clear();
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/**
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@brief dumps the %cudaFlow graph into a DOT format through an
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output stream
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*/
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void dump(std::ostream& os) const;
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/**
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@brief dumps the native CUDA graph into a DOT format through an
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output stream
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The native CUDA graph may be different from the upper-level %cudaFlow
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graph when flow capture is involved.
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*/
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void dump_native_graph(std::ostream& os) const;
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// ------------------------------------------------------------------------
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// Graph building routines
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// ------------------------------------------------------------------------
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/**
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@brief creates a no-operation task
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@return a tf::cudaTask handle
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An empty node performs no operation during execution,
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but can be used for transitive ordering.
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For example, a phased execution graph with 2 groups of @c n nodes
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with a barrier between them can be represented using an empty node
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and @c 2*n dependency edges,
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rather than no empty node and @c n^2 dependency edges.
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*/
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cudaTask noop();
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/**
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@brief creates a host task that runs a callable on the host
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@tparam C callable type
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@param callable a callable object with neither arguments nor return
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(i.e., constructible from @c std::function<void()>)
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@return a tf::cudaTask handle
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A host task can only execute CPU-specific functions and cannot do any CUDA calls
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(e.g., @c cudaMalloc).
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*/
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template <typename C>
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cudaTask host(C&& callable);
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/**
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@brief updates parameters of a host task
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The method is similar to tf::cudaFlow::host but operates on a task
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of type tf::cudaTaskType::HOST.
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*/
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template <typename C>
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void host(cudaTask task, C&& callable);
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/**
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@brief creates a kernel task
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@tparam F kernel function type
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@tparam ArgsT kernel function parameters type
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@param g configured grid
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@param b configured block
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@param s configured shared memory size in bytes
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@param f kernel function
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@param args arguments to forward to the kernel function by copy
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@return a tf::cudaTask handle
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*/
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template <typename F, typename... ArgsT>
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cudaTask kernel(dim3 g, dim3 b, size_t s, F f, ArgsT... args);
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/**
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@brief updates parameters of a kernel task
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The method is similar to tf::cudaFlow::kernel but operates on a task
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of type tf::cudaTaskType::KERNEL.
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The kernel function name must NOT change.
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*/
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template <typename F, typename... ArgsT>
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void kernel(
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cudaTask task, dim3 g, dim3 b, size_t shm, F f, ArgsT... args
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);
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/**
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@brief creates a memset task that fills untyped data with a byte value
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@param dst pointer to the destination device memory area
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@param v value to set for each byte of specified memory
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@param count size in bytes to set
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@return a tf::cudaTask handle
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A memset task fills the first @c count bytes of device memory area
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pointed by @c dst with the byte value @c v.
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*/
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cudaTask memset(void* dst, int v, size_t count);
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/**
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@brief updates parameters of a memset task
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The method is similar to tf::cudaFlow::memset but operates on a task
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of type tf::cudaTaskType::MEMSET.
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The source/destination memory may have different address values but
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must be allocated from the same contexts as the original
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source/destination memory.
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*/
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void memset(cudaTask task, void* dst, int ch, size_t count);
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/**
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@brief creates a memcpy task that copies untyped data in bytes
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@param tgt pointer to the target memory block
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@param src pointer to the source memory block
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@param bytes bytes to copy
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@return a tf::cudaTask handle
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A memcpy task transfers @c bytes of data from a source location
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to a target location. Direction can be arbitrary among CPUs and GPUs.
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*/
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cudaTask memcpy(void* tgt, const void* src, size_t bytes);
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/**
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@brief updates parameters of a memcpy task
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The method is similar to tf::cudaFlow::memcpy but operates on a task
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of type tf::cudaTaskType::MEMCPY.
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The source/destination memory may have different address values but
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must be allocated from the same contexts as the original
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source/destination memory.
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*/
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void memcpy(cudaTask task, void* tgt, const void* src, size_t bytes);
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/**
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@brief creates a memset task that sets a typed memory block to zero
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@tparam T element type (size of @c T must be either 1, 2, or 4)
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@param dst pointer to the destination device memory area
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@param count number of elements
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@return a tf::cudaTask handle
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A zero task zeroes the first @c count elements of type @c T
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in a device memory area pointed by @c dst.
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*/
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template <typename T, std::enable_if_t<
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is_pod_v<T> && (sizeof(T)==1 || sizeof(T)==2 || sizeof(T)==4), void>* = nullptr
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>
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cudaTask zero(T* dst, size_t count);
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/**
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@brief updates parameters of a memset task to a zero task
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The method is similar to tf::cudaFlow::zero but operates on
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a task of type tf::cudaTaskType::MEMSET.
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The source/destination memory may have different address values but
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must be allocated from the same contexts as the original
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source/destination memory.
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*/
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template <typename T, std::enable_if_t<
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is_pod_v<T> && (sizeof(T)==1 || sizeof(T)==2 || sizeof(T)==4), void>* = nullptr
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>
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void zero(cudaTask task, T* dst, size_t count);
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/**
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@brief creates a memset task that fills a typed memory block with a value
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@tparam T element type (size of @c T must be either 1, 2, or 4)
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@param dst pointer to the destination device memory area
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@param value value to fill for each element of type @c T
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@param count number of elements
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@return a tf::cudaTask handle
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A fill task fills the first @c count elements of type @c T with @c value
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in a device memory area pointed by @c dst.
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The value to fill is interpreted in type @c T rather than byte.
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*/
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template <typename T, std::enable_if_t<
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is_pod_v<T> && (sizeof(T)==1 || sizeof(T)==2 || sizeof(T)==4), void>* = nullptr
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>
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cudaTask fill(T* dst, T value, size_t count);
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/**
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@brief updates parameters of a memset task to a fill task
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The method is similar to tf::cudaFlow::fill but operates on a task
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of type tf::cudaTaskType::MEMSET.
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The source/destination memory may have different address values but
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must be allocated from the same contexts as the original
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source/destination memory.
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*/
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template <typename T, std::enable_if_t<
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is_pod_v<T> && (sizeof(T)==1 || sizeof(T)==2 || sizeof(T)==4), void>* = nullptr
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>
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void fill(cudaTask task, T* dst, T value, size_t count);
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/**
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@brief creates a memcopy task that copies typed data
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@tparam T element type (non-void)
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@param tgt pointer to the target memory block
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@param src pointer to the source memory block
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@param num number of elements to copy
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@return a tf::cudaTask handle
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A copy task transfers <tt>num*sizeof(T)</tt> bytes of data from a source location
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to a target location. Direction can be arbitrary among CPUs and GPUs.
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*/
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template <typename T,
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std::enable_if_t<!std::is_same_v<T, void>, void>* = nullptr
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>
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cudaTask copy(T* tgt, const T* src, size_t num);
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/**
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@brief updates parameters of a memcpy task to a copy task
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The method is similar to tf::cudaFlow::copy but operates on a task
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of type tf::cudaTaskType::MEMCPY.
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The source/destination memory may have different address values but
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must be allocated from the same contexts as the original
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source/destination memory.
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*/
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template <typename T,
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std::enable_if_t<!std::is_same_v<T, void>, void>* = nullptr
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>
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void copy(cudaTask task, T* tgt, const T* src, size_t num);
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// ------------------------------------------------------------------------
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// run method
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// ------------------------------------------------------------------------
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/**
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@brief offloads the %cudaFlow onto a GPU asynchronously via a stream
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@param stream stream for performing this operation
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Offloads the present %cudaFlow onto a GPU asynchronously via
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the given stream.
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An offloaded %cudaFlow forces the underlying graph to be instantiated.
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After the instantiation, you should not modify the graph topology
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but update node parameters.
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*/
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void run(cudaStream_t stream);
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/**
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@brief acquires a reference to the underlying CUDA graph
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*/
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cudaGraph_t native_graph();
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/**
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@brief acquires a reference to the underlying CUDA graph executable
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*/
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cudaGraphExec_t native_executable();
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// ------------------------------------------------------------------------
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// generic algorithms
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// ------------------------------------------------------------------------
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/**
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@brief runs a callable with only a single kernel thread
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@tparam C callable type
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@param c callable to run by a single kernel thread
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@return a tf::cudaTask handle
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*/
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template <typename C>
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cudaTask single_task(C c);
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/**
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@brief updates a single-threaded kernel task
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This method is similar to cudaFlow::single_task but operates
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on an existing task.
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*/
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template <typename C>
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void single_task(cudaTask task, C c);
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/**
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@brief applies a callable to each dereferenced element of the data array
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@tparam I iterator type
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@tparam C callable type
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@param first iterator to the beginning (inclusive)
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@param last iterator to the end (exclusive)
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@param callable a callable object to apply to the dereferenced iterator
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@return a tf::cudaTask handle
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This method is equivalent to the parallel execution of the following loop on a GPU:
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@code{.cpp}
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for(auto itr = first; itr != last; itr++) {
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callable(*itr);
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}
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@endcode
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*/
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template <typename I, typename C>
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cudaTask for_each(I first, I last, C callable);
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/**
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@brief updates parameters of a kernel task created from
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tf::cudaFlow::for_each
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The type of the iterators and the callable must be the same as
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the task created from tf::cudaFlow::for_each.
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*/
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template <typename I, typename C>
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void for_each(cudaTask task, I first, I last, C callable);
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/**
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@brief applies a callable to each index in the range with the step size
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@tparam I index type
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@tparam C callable type
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@param first beginning index
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@param last last index
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@param step step size
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@param callable the callable to apply to each element in the data array
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@return a tf::cudaTask handle
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This method is equivalent to the parallel execution of the following loop on a GPU:
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@code{.cpp}
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// step is positive [first, last)
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for(auto i=first; i<last; i+=step) {
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callable(i);
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}
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// step is negative [first, last)
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for(auto i=first; i>last; i+=step) {
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callable(i);
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}
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@endcode
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*/
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template <typename I, typename C>
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cudaTask for_each_index(I first, I last, I step, C callable);
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/**
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@brief updates parameters of a kernel task created from
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tf::cudaFlow::for_each_index
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The type of the iterators and the callable must be the same as
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the task created from tf::cudaFlow::for_each_index.
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*/
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template <typename I, typename C>
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void for_each_index(
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cudaTask task, I first, I last, I step, C callable
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);
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/**
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@brief applies a callable to a source range and stores the result in a target range
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@tparam I input iterator type
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@tparam O output iterator type
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@tparam C unary operator type
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@param first iterator to the beginning of the input range
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@param last iterator to the end of the input range
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@param output iterator to the beginning of the output range
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@param op the operator to apply to transform each element in the range
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@return a tf::cudaTask handle
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This method is equivalent to the parallel execution of the following loop on a GPU:
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@code{.cpp}
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while (first != last) {
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*output++ = callable(*first++);
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}
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@endcode
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*/
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template <typename I, typename O, typename C>
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cudaTask transform(I first, I last, O output, C op);
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/**
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@brief updates parameters of a kernel task created from
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tf::cudaFlow::transform
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The type of the iterators and the callable must be the same as
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the task created from tf::cudaFlow::for_each.
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*/
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template <typename I, typename O, typename C>
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void transform(cudaTask task, I first, I last, O output, C c);
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/**
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@brief creates a task to perform parallel transforms over two ranges of items
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@tparam I1 first input iterator type
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@tparam I2 second input iterator type
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@tparam O output iterator type
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@tparam C unary operator type
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@param first1 iterator to the beginning of the input range
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@param last1 iterator to the end of the input range
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@param first2 iterato
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@param output iterator to the beginning of the output range
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@param op binary operator to apply to transform each pair of items in the
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two input ranges
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@return cudaTask handle
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This method is equivalent to the parallel execution of the following loop on a GPU:
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@code{.cpp}
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while (first1 != last1) {
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*output++ = op(*first1++, *first2++);
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}
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@endcode
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*/
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template <typename I1, typename I2, typename O, typename C>
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cudaTask transform(I1 first1, I1 last1, I2 first2, O output, C op);
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/**
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@brief updates parameters of a kernel task created from
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tf::cudaFlow::transform
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The type of the iterators and the callable must be the same as
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the task created from tf::cudaFlow::for_each.
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*/
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template <typename I1, typename I2, typename O, typename C>
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void transform(
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cudaTask task, I1 first1, I1 last1, I2 first2, O output, C c
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);
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// ------------------------------------------------------------------------
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// subflow
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// ------------------------------------------------------------------------
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/**
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@brief constructs a subflow graph through tf::cudaFlowCapturer
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@tparam C callable type constructible from
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@c std::function<void(tf::cudaFlowCapturer&)>
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@param callable the callable to construct a capture flow
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@return a tf::cudaTask handle
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A captured subflow forms a sub-graph to the %cudaFlow and can be used to
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capture custom (or third-party) kernels that cannot be directly constructed
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from the %cudaFlow.
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Example usage:
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@code{.cpp}
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taskflow.emplace([&](tf::cudaFlow& cf){
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tf::cudaTask my_kernel = cf.kernel(my_arguments);
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// create a flow capturer to capture custom kernels
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tf::cudaTask my_subflow = cf.capture([&](tf::cudaFlowCapturer& capturer){
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capturer.on([&](cudaStream_t stream){
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invoke_custom_kernel_with_stream(stream, custom_arguments);
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});
|
|
});
|
|
|
|
my_kernel.precede(my_subflow);
|
|
});
|
|
@endcode
|
|
*/
|
|
template <typename C>
|
|
cudaTask capture(C&& callable);
|
|
|
|
/**
|
|
@brief updates the captured child graph
|
|
|
|
The method is similar to tf::cudaFlow::capture but operates on a task
|
|
of type tf::cudaTaskType::SUBFLOW.
|
|
The new captured graph must be topologically identical to the original
|
|
captured graph.
|
|
*/
|
|
template <typename C>
|
|
void capture(cudaTask task, C callable);
|
|
|
|
private:
|
|
|
|
cudaFlowGraph _cfg;
|
|
cudaGraphExec _exe {nullptr};
|
|
};
|
|
|
|
// Construct a standalone cudaFlow
|
|
inline cudaFlow::cudaFlow() {
|
|
_cfg._native_handle.create();
|
|
}
|
|
|
|
// Procedure: clear
|
|
inline void cudaFlow::clear() {
|
|
_exe.clear();
|
|
_cfg.clear();
|
|
_cfg._native_handle.create();
|
|
}
|
|
|
|
// Function: empty
|
|
inline bool cudaFlow::empty() const {
|
|
return _cfg._nodes.empty();
|
|
}
|
|
|
|
// Function: num_tasks
|
|
inline size_t cudaFlow::num_tasks() const {
|
|
return _cfg._nodes.size();
|
|
}
|
|
|
|
// Procedure: dump
|
|
inline void cudaFlow::dump(std::ostream& os) const {
|
|
_cfg.dump(os, nullptr, "");
|
|
}
|
|
|
|
// Procedure: dump
|
|
inline void cudaFlow::dump_native_graph(std::ostream& os) const {
|
|
cuda_dump_graph(os, _cfg._native_handle);
|
|
}
|
|
|
|
// ----------------------------------------------------------------------------
|
|
// Graph building methods
|
|
// ----------------------------------------------------------------------------
|
|
|
|
// Function: noop
|
|
inline cudaTask cudaFlow::noop() {
|
|
|
|
auto node = _cfg.emplace_back(
|
|
_cfg, std::in_place_type_t<cudaFlowNode::Empty>{}
|
|
);
|
|
|
|
TF_CHECK_CUDA(
|
|
cudaGraphAddEmptyNode(
|
|
&node->_native_handle, _cfg._native_handle, nullptr, 0
|
|
),
|
|
"failed to create a no-operation (empty) node"
|
|
);
|
|
|
|
return cudaTask(node);
|
|
}
|
|
|
|
// Function: host
|
|
template <typename C>
|
|
cudaTask cudaFlow::host(C&& c) {
|
|
|
|
auto node = _cfg.emplace_back(
|
|
_cfg, std::in_place_type_t<cudaFlowNode::Host>{}, std::forward<C>(c)
|
|
);
|
|
|
|
auto h = std::get_if<cudaFlowNode::Host>(&node->_handle);
|
|
|
|
cudaHostNodeParams p;
|
|
p.fn = cudaFlowNode::Host::callback;
|
|
p.userData = h;
|
|
|
|
TF_CHECK_CUDA(
|
|
cudaGraphAddHostNode(
|
|
&node->_native_handle, _cfg._native_handle, nullptr, 0, &p
|
|
),
|
|
"failed to create a host node"
|
|
);
|
|
|
|
return cudaTask(node);
|
|
}
|
|
|
|
// Function: kernel
|
|
template <typename F, typename... ArgsT>
|
|
cudaTask cudaFlow::kernel(
|
|
dim3 g, dim3 b, size_t s, F f, ArgsT... args
|
|
) {
|
|
|
|
auto node = _cfg.emplace_back(
|
|
_cfg, std::in_place_type_t<cudaFlowNode::Kernel>{}, (void*)f
|
|
);
|
|
|
|
cudaKernelNodeParams p;
|
|
void* arguments[sizeof...(ArgsT)] = { (void*)(&args)... };
|
|
p.func = (void*)f;
|
|
p.gridDim = g;
|
|
p.blockDim = b;
|
|
p.sharedMemBytes = s;
|
|
p.kernelParams = arguments;
|
|
p.extra = nullptr;
|
|
|
|
TF_CHECK_CUDA(
|
|
cudaGraphAddKernelNode(
|
|
&node->_native_handle, _cfg._native_handle, nullptr, 0, &p
|
|
),
|
|
"failed to create a kernel task"
|
|
);
|
|
|
|
return cudaTask(node);
|
|
}
|
|
|
|
// Function: zero
|
|
template <typename T, std::enable_if_t<
|
|
is_pod_v<T> && (sizeof(T)==1 || sizeof(T)==2 || sizeof(T)==4), void>*
|
|
>
|
|
cudaTask cudaFlow::zero(T* dst, size_t count) {
|
|
|
|
auto node = _cfg.emplace_back(
|
|
_cfg, std::in_place_type_t<cudaFlowNode::Memset>{}
|
|
);
|
|
|
|
auto p = cuda_get_zero_parms(dst, count);
|
|
|
|
TF_CHECK_CUDA(
|
|
cudaGraphAddMemsetNode(
|
|
&node->_native_handle, _cfg._native_handle, nullptr, 0, &p
|
|
),
|
|
"failed to create a memset (zero) task"
|
|
);
|
|
|
|
return cudaTask(node);
|
|
}
|
|
|
|
// Function: fill
|
|
template <typename T, std::enable_if_t<
|
|
is_pod_v<T> && (sizeof(T)==1 || sizeof(T)==2 || sizeof(T)==4), void>*
|
|
>
|
|
cudaTask cudaFlow::fill(T* dst, T value, size_t count) {
|
|
|
|
auto node = _cfg.emplace_back(
|
|
_cfg, std::in_place_type_t<cudaFlowNode::Memset>{}
|
|
);
|
|
|
|
auto p = cuda_get_fill_parms(dst, value, count);
|
|
|
|
TF_CHECK_CUDA(
|
|
cudaGraphAddMemsetNode(
|
|
&node->_native_handle, _cfg._native_handle, nullptr, 0, &p
|
|
),
|
|
"failed to create a memset (fill) task"
|
|
);
|
|
|
|
return cudaTask(node);
|
|
}
|
|
|
|
// Function: copy
|
|
template <
|
|
typename T,
|
|
std::enable_if_t<!std::is_same_v<T, void>, void>*
|
|
>
|
|
cudaTask cudaFlow::copy(T* tgt, const T* src, size_t num) {
|
|
|
|
auto node = _cfg.emplace_back(
|
|
_cfg, std::in_place_type_t<cudaFlowNode::Memcpy>{}
|
|
);
|
|
|
|
auto p = cuda_get_copy_parms(tgt, src, num);
|
|
|
|
TF_CHECK_CUDA(
|
|
cudaGraphAddMemcpyNode(
|
|
&node->_native_handle, _cfg._native_handle, nullptr, 0, &p
|
|
),
|
|
"failed to create a memcpy (copy) task"
|
|
);
|
|
|
|
return cudaTask(node);
|
|
}
|
|
|
|
// Function: memset
|
|
inline cudaTask cudaFlow::memset(void* dst, int ch, size_t count) {
|
|
|
|
auto node = _cfg.emplace_back(
|
|
_cfg, std::in_place_type_t<cudaFlowNode::Memset>{}
|
|
);
|
|
|
|
auto p = cuda_get_memset_parms(dst, ch, count);
|
|
|
|
TF_CHECK_CUDA(
|
|
cudaGraphAddMemsetNode(
|
|
&node->_native_handle, _cfg._native_handle, nullptr, 0, &p
|
|
),
|
|
"failed to create a memset task"
|
|
);
|
|
|
|
return cudaTask(node);
|
|
}
|
|
|
|
// Function: memcpy
|
|
inline cudaTask cudaFlow::memcpy(void* tgt, const void* src, size_t bytes) {
|
|
|
|
auto node = _cfg.emplace_back(
|
|
_cfg, std::in_place_type_t<cudaFlowNode::Memcpy>{}
|
|
);
|
|
|
|
auto p = cuda_get_memcpy_parms(tgt, src, bytes);
|
|
|
|
TF_CHECK_CUDA(
|
|
cudaGraphAddMemcpyNode(
|
|
&node->_native_handle, _cfg._native_handle, nullptr, 0, &p
|
|
),
|
|
"failed to create a memcpy task"
|
|
);
|
|
|
|
return cudaTask(node);
|
|
}
|
|
|
|
// ------------------------------------------------------------------------
|
|
// update methods
|
|
// ------------------------------------------------------------------------
|
|
|
|
// Function: host
|
|
template <typename C>
|
|
void cudaFlow::host(cudaTask task, C&& c) {
|
|
|
|
if(task.type() != cudaTaskType::HOST) {
|
|
TF_THROW(task, " is not a host task");
|
|
}
|
|
|
|
auto h = std::get_if<cudaFlowNode::Host>(&task._node->_handle);
|
|
|
|
h->func = std::forward<C>(c);
|
|
}
|
|
|
|
// Function: update kernel parameters
|
|
template <typename F, typename... ArgsT>
|
|
void cudaFlow::kernel(
|
|
cudaTask task, dim3 g, dim3 b, size_t s, F f, ArgsT... args
|
|
) {
|
|
|
|
if(task.type() != cudaTaskType::KERNEL) {
|
|
TF_THROW(task, " is not a kernel task");
|
|
}
|
|
|
|
cudaKernelNodeParams p;
|
|
|
|
void* arguments[sizeof...(ArgsT)] = { (void*)(&args)... };
|
|
p.func = (void*)f;
|
|
p.gridDim = g;
|
|
p.blockDim = b;
|
|
p.sharedMemBytes = s;
|
|
p.kernelParams = arguments;
|
|
p.extra = nullptr;
|
|
|
|
TF_CHECK_CUDA(
|
|
cudaGraphExecKernelNodeSetParams(_exe, task._node->_native_handle, &p),
|
|
"failed to update kernel parameters on ", task
|
|
);
|
|
}
|
|
|
|
// Function: update copy parameters
|
|
template <typename T, std::enable_if_t<!std::is_same_v<T, void>, void>*>
|
|
void cudaFlow::copy(cudaTask task, T* tgt, const T* src, size_t num) {
|
|
|
|
if(task.type() != cudaTaskType::MEMCPY) {
|
|
TF_THROW(task, " is not a memcpy task");
|
|
}
|
|
|
|
auto p = cuda_get_copy_parms(tgt, src, num);
|
|
|
|
TF_CHECK_CUDA(
|
|
cudaGraphExecMemcpyNodeSetParams(_exe, task._node->_native_handle, &p),
|
|
"failed to update memcpy parameters on ", task
|
|
);
|
|
}
|
|
|
|
// Function: update memcpy parameters
|
|
inline void cudaFlow::memcpy(
|
|
cudaTask task, void* tgt, const void* src, size_t bytes
|
|
) {
|
|
|
|
if(task.type() != cudaTaskType::MEMCPY) {
|
|
TF_THROW(task, " is not a memcpy task");
|
|
}
|
|
|
|
auto p = cuda_get_memcpy_parms(tgt, src, bytes);
|
|
|
|
TF_CHECK_CUDA(
|
|
cudaGraphExecMemcpyNodeSetParams(_exe, task._node->_native_handle, &p),
|
|
"failed to update memcpy parameters on ", task
|
|
);
|
|
}
|
|
|
|
// Procedure: memset
|
|
inline void cudaFlow::memset(cudaTask task, void* dst, int ch, size_t count) {
|
|
|
|
if(task.type() != cudaTaskType::MEMSET) {
|
|
TF_THROW(task, " is not a memset task");
|
|
}
|
|
|
|
auto p = cuda_get_memset_parms(dst, ch, count);
|
|
|
|
TF_CHECK_CUDA(
|
|
cudaGraphExecMemsetNodeSetParams(_exe, task._node->_native_handle, &p),
|
|
"failed to update memset parameters on ", task
|
|
);
|
|
}
|
|
|
|
// Procedure: fill
|
|
template <typename T, std::enable_if_t<
|
|
is_pod_v<T> && (sizeof(T)==1 || sizeof(T)==2 || sizeof(T)==4), void>*
|
|
>
|
|
void cudaFlow::fill(cudaTask task, T* dst, T value, size_t count) {
|
|
|
|
if(task.type() != cudaTaskType::MEMSET) {
|
|
TF_THROW(task, " is not a memset task");
|
|
}
|
|
|
|
auto p = cuda_get_fill_parms(dst, value, count);
|
|
|
|
TF_CHECK_CUDA(
|
|
cudaGraphExecMemsetNodeSetParams(_exe, task._node->_native_handle, &p),
|
|
"failed to update memset parameters on ", task
|
|
);
|
|
}
|
|
|
|
// Procedure: zero
|
|
template <typename T, std::enable_if_t<
|
|
is_pod_v<T> && (sizeof(T)==1 || sizeof(T)==2 || sizeof(T)==4), void>*
|
|
>
|
|
void cudaFlow::zero(cudaTask task, T* dst, size_t count) {
|
|
|
|
if(task.type() != cudaTaskType::MEMSET) {
|
|
TF_THROW(task, " is not a memset task");
|
|
}
|
|
|
|
auto p = cuda_get_zero_parms(dst, count);
|
|
|
|
TF_CHECK_CUDA(
|
|
cudaGraphExecMemsetNodeSetParams(_exe, task._node->_native_handle, &p),
|
|
"failed to update memset parameters on ", task
|
|
);
|
|
}
|
|
|
|
// Function: capture
|
|
template <typename C>
|
|
void cudaFlow::capture(cudaTask task, C c) {
|
|
|
|
if(task.type() != cudaTaskType::SUBFLOW) {
|
|
TF_THROW(task, " is not a subflow task");
|
|
}
|
|
|
|
// insert a subflow node
|
|
// construct a captured flow from the callable
|
|
auto node_handle = std::get_if<cudaFlowNode::Subflow>(&task._node->_handle);
|
|
//node_handle->graph.clear();
|
|
|
|
cudaFlowCapturer capturer;
|
|
c(capturer);
|
|
|
|
// obtain the optimized captured graph
|
|
capturer._cfg._native_handle.reset(capturer.capture());
|
|
node_handle->cfg = std::move(capturer._cfg);
|
|
|
|
TF_CHECK_CUDA(
|
|
cudaGraphExecChildGraphNodeSetParams(
|
|
_exe,
|
|
task._node->_native_handle,
|
|
node_handle->cfg._native_handle
|
|
),
|
|
"failed to update a captured child graph"
|
|
);
|
|
}
|
|
|
|
// ----------------------------------------------------------------------------
|
|
// captured flow
|
|
// ----------------------------------------------------------------------------
|
|
|
|
// Function: capture
|
|
template <typename C>
|
|
cudaTask cudaFlow::capture(C&& c) {
|
|
|
|
// insert a subflow node
|
|
auto node = _cfg.emplace_back(
|
|
_cfg, std::in_place_type_t<cudaFlowNode::Subflow>{}
|
|
);
|
|
|
|
// construct a captured flow from the callable
|
|
auto node_handle = std::get_if<cudaFlowNode::Subflow>(&node->_handle);
|
|
|
|
// perform capturing
|
|
cudaFlowCapturer capturer;
|
|
c(capturer);
|
|
|
|
// obtain the optimized captured graph
|
|
capturer._cfg._native_handle.reset(capturer.capture());
|
|
|
|
// move capturer's cudaFlow graph into node
|
|
node_handle->cfg = std::move(capturer._cfg);
|
|
|
|
TF_CHECK_CUDA(
|
|
cudaGraphAddChildGraphNode(
|
|
&node->_native_handle,
|
|
_cfg._native_handle,
|
|
nullptr,
|
|
0,
|
|
node_handle->cfg._native_handle
|
|
),
|
|
"failed to add a cudaFlow capturer task"
|
|
);
|
|
|
|
return cudaTask(node);
|
|
}
|
|
|
|
// ----------------------------------------------------------------------------
|
|
// run method
|
|
// ----------------------------------------------------------------------------
|
|
|
|
// Procedure: run
|
|
inline void cudaFlow::run(cudaStream_t stream) {
|
|
if(!_exe) {
|
|
_exe.instantiate(_cfg._native_handle);
|
|
}
|
|
_exe.launch(stream);
|
|
_cfg._state = cudaFlowGraph::OFFLOADED;
|
|
}
|
|
|
|
// Function: native_cfg
|
|
inline cudaGraph_t cudaFlow::native_graph() {
|
|
return _cfg._native_handle;
|
|
}
|
|
|
|
// Function: native_executable
|
|
inline cudaGraphExec_t cudaFlow::native_executable() {
|
|
return _exe;
|
|
}
|
|
|
|
} // end of namespace tf -----------------------------------------------------
|
|
|
|
|