# cpp-taskflow
**Repository Path**: duohafa/cpp-taskflow
## Basic Information
- **Project Name**: cpp-taskflow
- **Description**: cpp-taskflow 是一个开源的 C++ 并行任务编程库，cpp-tastflow 非常快，只包含头文件，可以帮你快速编写包含复杂任务依赖的并行程序
- **Primary Language**: C/C++
- **License**: MIT
- **Default Branch**: master
- **Homepage**: https://www.oschina.net/p/cpp-taskflow
- **GVP Project**: No
## Statistics
- **Stars**: 1
- **Forks**: 34
- **Created**: 2020-03-27
- **Last Updated**: 2024-05-30
## Categories & Tags
**Categories**: Uncategorized
**Tags**: None
## README
# Cpp-Taskflow 
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[](https://travis-ci.com/cpp-taskflow/cpp-taskflow)
[](https://ci.appveyor.com/project/TsungWeiHuang/cpp-taskflow)
[][wiki]
[](doxygen/reference/ipdps19.pdf)
A header-only C++ library to help you quickly write parallel and heterogeneous programs using task models
# Why Cpp-Taskflow?
Cpp-Taskflow is by far faster, more expressive, and easier for drop-in integration
than existing task programming libraries such as [OpenMP Tasking][OpenMP Tasking]
and [Intel TBB FlowGraph][TBB FlowGraph] in handling complex parallel workloads.
Cpp-Taskflow lets you quickly implement task decomposition strategies
that incorporate both regular and irregular compute patterns,
together with an efficient *work-stealing* scheduler to optimize your multithreaded performance.
| Without Cpp-Taskflow | With Cpp-Taskflow |
| -------------------- | ----------------- |
|  |  |
Cpp-Taskflow has a unified interface for both *static* tasking and *dynamic* tasking,
allowing users to quickly master our parallel task programming model in a natural idiom.
| [Static Tasking](#get-started-with-cpp-taskflow) | [Dynamic Tasking](#dynamic-tasking) |
| :------------: | :-------------: |
|  | 
|
Cpp-Taskflow supports conditional tasking for you to implement cyclic and dynamic control flows that are otherwise difficult to do with existing task programming frameworks.
| [Conditional Tasking](#conditional-tasking) |
| :-----------------: |
|  |
Cpp-Taskflow is composable. You can create large parallel graphs through
composition of modular and reusable blocks that are easier to optimize
at an individual scope.
| [Taskflow Composition](#composable-tasking) |
| :---------------: |
||
Cpp-Taskflow supports heterogeneous tasking for you to
accelerate a wide range of scientific computing applications
by harnessing the power of CPU-GPU collaborative computing.
| [Concurrent CPU-GPU Tasking](#concurrent-cpu-gpu-tasking) |
| :-----------------: |
|  |
We are committed to support trustworthy developments for both academic and industrial research projects in parallel computing. Check out [Who is Using Cpp-Taskflow](#who-is-using-cpp-taskflow) and what our users say:
+ *"Cpp-Taskflow is the cleanest Task API I've ever seen." [damienhocking][damienhocking]*
+ *"Cpp-Taskflow has a very simple and elegant tasking interface. The performance also scales very well." [totalgee][totalgee]*
+ *"Cpp-Taskflow lets me handle parallel processing in a smart way." [Hayabusa](https://cpp-learning.com/cpp-taskflow/)*
+ *"Best poster award for open-source parallel programming library." [Cpp Conference 2018][Cpp Conference 2018]*
+ *"Second Prize of Open-source Software Competition." [ACM Multimedia Conference 2019](https://tsung-wei-huang.github.io/img/mm19-ossc-award.jpg)*
See a quick [presentation][Presentation] and
visit the [documentation][wiki] to learn more about Cpp-Taskflow.
Technical details can be referred to our [IEEE IPDPS19 paper][IPDPS19].
# Table of Contents
* [Get Started with Cpp-Taskflow](#get-started-with-cpp-taskflow)
* [Create a Taskflow Application](#create-a-taskflow-application)
* [Step 1: Create a Taskflow](#step-1-create-a-taskflow)
* [Step 2: Define Task Dependencies](#step-2-define-task-dependencies)
* [Step 3: Execute a Taskflow](#step-3-execute-a-taskflow)
* [Dynamic Tasking](#dynamic-tasking)
* [Conditional Tasking](#conditional-tasking)
* [Step 1: Create a Condition Task](#step-1-create-a-condition-task)
* [Step 2: Scheduling Rules for Condition Tasks](#step-2-scheduling-rules-for-condition-tasks)
* [Composable Tasking](#composable-tasking)
* [Concurrent CPU-GPU Tasking](#concurrent-cpu-gpu-tasking)
* [Step 1: Create a cudaFlow](#step-1-create-a-cudaflow)
* [Step 2: Compile and Execute a cudaFlow](#step-2-compile-and-execute-a-cudaflow)
* [Visualize a Taskflow Graph](#visualize-a-taskflow-graph)
* [Monitor Thread Activities](#monitor-thread-activities)
* [API Reference](#api-reference)
* [System Requirements](#system-requirements)
* [Compile Unit Tests, Examples, and Benchmarks](#compile-unit-tests-examples-and-benchmarks)
* [Who is Using Cpp-Taskflow?](#who-is-using-cpp-taskflow)
# Get Started with Cpp-Taskflow
The following example [simple.cpp](./examples/simple.cpp) shows the basic Cpp-Taskflow API
you need in most applications.
```cpp
#include // Cpp-Taskflow is header-only
int main(){
tf::Executor executor;
tf::Taskflow taskflow;
auto [A, B, C, D] = taskflow.emplace(
[] () { std::cout << "TaskA\n"; }, // task dependency graph
[] () { std::cout << "TaskB\n"; }, //
[] () { std::cout << "TaskC\n"; }, // +---+
[] () { std::cout << "TaskD\n"; } // +---->| B |-----+
); // | +---+ |
// +---+ +-v-+
A.precede(B); // A runs before B // | A | | D |
A.precede(C); // A runs before C // +---+ +-^-+
B.precede(D); // B runs before D // | +---+ |
C.precede(D); // C runs before D // +---->| C |-----+
// +---+
executor.run(taskflow).wait();
return 0;
}
```
Compile and run the code with the following commands:
```bash
~$ g++ simple.cpp -I path/to/include/taskflow/ -std=c++17 -O2 -lpthread -o simple
~$ ./simple
TaskA
TaskC <-- concurrent with TaskB
TaskB <-- concurrent with TaskC
TaskD
```
# Create a Taskflow Application
Cpp-Taskflow defines a very expressive API to create task dependency graphs.
Most applications are developed through the following three steps:
## Step 1: Create a Taskflow
Create a taskflow object to build a task dependency graph:
```cpp
tf::Taskflow taskflow;
```
A task is a callable object for which [std::invoke][std::invoke] is applicable.
Use the method `emplace` to create a task:
```cpp
tf::Task A = taskflow.emplace([](){ std::cout << "Task A\n"; });
```
## Step 2: Define Task Dependencies
You can add dependency links between tasks to enforce one task to run before or
after another.
```cpp
A.precede(B); // A runs before B.
```
## Step 3: Execute a Taskflow
To execute a taskflow, you need to create an *executor*.
An executor manages a set of worker threads to execute a taskflow
through an efficient *work-stealing* algorithm.
```cpp
tf::Executor executor;
```
The executor provides a rich set of methods to run a taskflow.
You can run a taskflow multiple times, or until a stopping criteria is met.
These methods are non-blocking with a [std::future][std::future] return
to let you query the execution status.
Executor is *thread-safe*.
```cpp
executor.run(taskflow); // runs the taskflow once
executor.run_n(taskflow, 4); // runs the taskflow four times
// keeps running the taskflow until the predicate becomes true
executor.run_until(taskflow, [counter=4](){ return --counter == 0; } );
```
You can call `wait_for_all` to block the executor until all associated taskflows complete.
```cpp
executor.wait_for_all(); // block until all associated tasks finish
```
Notice that the executor does not own any taskflow.
It is your responsibility to keep a taskflow alive during its execution,
or it can result in undefined behavior.
In most applications, you need only one executor to run multiple taskflows
each representing a specific part of your parallel decomposition.
# Dynamic Tasking
Another powerful feature of Taskflow is *dynamic* tasking.
Dynamic tasks are those tasks created during the execution of a taskflow.
These tasks are spawned by a parent task and are grouped together to a *subflow* graph.
To create a subflow for dynamic tasking,
emplace a callable with one argument of type `tf::Subflow`.
```cpp
// create three regular tasks
tf::Task A = tf.emplace([](){}).name("A");
tf::Task C = tf.emplace([](){}).name("C");
tf::Task D = tf.emplace([](){}).name("D");
// create a subflow graph (dynamic tasking)
tf::Task B = tf.emplace([] (tf::Subflow& subflow) {
tf::Task B1 = subflow.emplace([](){}).name("B1");
tf::Task B2 = subflow.emplace([](){}).name("B2");
tf::Task B3 = subflow.emplace([](){}).name("B3");
B1.precede(B3);
B2.precede(B3);
}).name("B");
A.precede(B); // B runs after A
A.precede(C); // C runs after A
B.precede(D); // D runs after B
C.precede(D); // D runs after C
```
By default, a subflow graph joins its parent node.
This ensures a subflow graph finishes before the successors of
its parent task.
You can disable this feature by calling `subflow.detach()`.
For example, detaching the above subflow will result in the following execution flow:
```cpp
// create a "detached" subflow graph (dynamic tasking)
tf::Task B = tf.emplace([] (tf::Subflow& subflow) {
tf::Task B1 = subflow.emplace([](){}).name("B1");
tf::Task B2 = subflow.emplace([](){}).name("B2");
tf::Task B3 = subflow.emplace([](){}).name("B3");
B1.precede(B3);
B2.precede(B3);
// detach the subflow to form a parallel execution line
subflow.detach();
}).name("B");
```
A subflow can be nested or recursive. You can create another subflow from
the execution of a subflow and so on.
# Conditional Tasking
Taskflow supports *conditional tasking* for users to implement *dynamic* and *cyclic* control flows.
You can create highly versatile and efficient parallel patterns through condition tasks.
## Step 1: Create a Condition Task
A *condition task* evalutes a set of instructions and returns an integer index
of the next immediate successor to execute.
The index is defined with respect to the order of its successor construction.
```cpp
tf::Task init = tf.emplace([](){ }).name("init");
tf::Task stop = tf.emplace([](){ }).name("stop");
// creates a condition task that returns 0 or 1
tf::Task cond = tf.emplace([](){
std::cout << "flipping a coin\n";
return rand() % 2;
}).name("cond");
// creates a feedback loop
init.precede(cond);
cond.precede(cond, stop); // cond--0-->cond, cond--1-->stop
executor.run(tf).wait();
```
If the return value from `cond` is 0, it loops back to itself, or otherwise to `stop`.
Cpp-Taskflow terms the preceding link from a condition task a *weak dependency*
(dashed lines above).
Others are *strong depedency* (solid lines above).
## Step 2: Scheduling Rules for Condition Tasks
When you submit a taskflow to an executor,
the scheduler starts with tasks of *zero dependency* (both weak and strong dependencies)
and continues to execute successive tasks whenever *strong dependencies* are met.
However,
the scheduler skips this rule for a condition task and jumps directly to its successor
indexed by the return value.
It is users' responsibility to ensure a taskflow is properly conditioned.
Top things to avoid include no source tasks to start with and task race.
The figure shows common pitfalls and their remedies.
In the risky scenario, task X may not be raced if P and M is exclusively
branching to X.
A good practice for avoiding mistakes of conditional tasking is to infer the execution flow of your graphs based on our scheduling rules.
Make sure there is no task race.
# Composable Tasking
A powerful feature of `tf::Taskflow` is composability.
You can create multiple task graphs from different parts of your workload
and use them to compose a large graph through the `composed_of` method.
```cpp
tf::Taskflow f1, f2;
auto [f1A, f1B] = f1.emplace(
[]() { std::cout << "Task f1A\n"; },
[]() { std::cout << "Task f1B\n"; }
);
auto [f2A, f2B, f2C] = f2.emplace(
[]() { std::cout << "Task f2A\n"; },
[]() { std::cout << "Task f2B\n"; },
[]() { std::cout << "Task f2C\n"; }
);
auto f1_module_task = f2.composed_of(f1);
f1_module_task.succeed(f2A, f2B)
.precede(f2C);
```
Similarly, `composed_of` returns a task handle and you can use
`precede` to create dependencies.
You can compose a taskflow from multiple taskflows and use the result
to compose a larger taskflow and so on.
# Concurrent CPU-GPU Tasking
Cpp-Taskflow enables concurrent CPU-GPU tasking by leveraging
[Nvidia CUDA Toolkit][cuda-toolkit].
You can harness the power of CPU-GPU collaborative computing
to implement heterogeneous decomposition algorithms.
## Step 1: Create a cudaFlow
A `tf::cudaFlow` is a graph object created at runtime
similar to dynamic tasking.
It manages a task node in a taskflow and associates it
with a [CUDA Graph][cudaGraph].
To create a cudaFlow, emplace a callable with an argument
of type `tf::cudaFlow`.
```cpp
tf::Taskflow taskflow;
tf::Executor executor;
const unsigned N = 1<<20; // size of the vector
std::vector hx(N, 1.0f), hy(N, 2.0f); // x and y vectors at host
float *dx{nullptr}, *dy{nullptr}; // x and y vectors at device
tf::Task allocate_x = taskflow.emplace([&](){ cudaMalloc(&dx, N*sizeof(float));});
tf::Task allocate_y = taskflow.emplace([&](){ cudaMalloc(&dy, N*sizeof(float));});
tf::Task cudaflow = taskflow.emplace([&](tf::cudaFlow& cf) {
tf::cudaTask h2d_x = cf.copy(dx, hx.data(), N); // host-to-device x data transfer
tf::cudaTask h2d_y = cf.copy(dy, hy.data(), N); // host-to-device y data transfer
tf::cudaTask d2h_x = cf.copy(hx.data(), dx, N); // device-to-host x data transfer
tf::cudaTask d2h_y = cf.copy(hy.data(), dy, N); // device-to-host y data transfer
// launch saxpy<<<(N+255)/256, 256, 0>>>(N, 2.0f, dx, dy)
tf::cudaTask kernel = cf.kernel((N+255)/256, 256, 0, saxpy, N, 2.0f, dx, dy);
kernel.succeed(h2d_x, h2d_y)
.precede(d2h_x, d2h_y);
});
cudaflow.succeed(allocate_x, allocate_y); // overlap data allocations
executor.run(taskflow).wait();
```
Assume our kernel implements the canonical saxpy operation
(single-precision A·X Plus Y) using the CUDA syntax.
```cpp
// saxpy (single-precision A·X Plus Y) kernel
__global__ void saxpy(
int n, float a, float *x, float *y
) {
// get the thread index
int i = blockIdx.x*blockDim.x + threadIdx.x;
if (i < n) {
y[i] = a*x[i] + y[i];
}
}
```
## Step 2: Compile and Execute a cudaFlow
Name you source with the extension `.cu`, let's say `saxpy.cu`,
and compile it through [nvcc][nvcc]:
```bash
~$ nvcc saxpy.cu -I path/to/include/taskflow -O2 -o saxpy
~$ ./saxpy
```
Our source autonomously enables cudaFlow for compilers that support
CUDA.
# Visualize a Taskflow Graph
You can dump a taskflow through a `std::ostream`
in [GraphViz][GraphViz] format using the method `dump`.
There are a number of free [GraphViz tools][AwesomeGraphViz] you could find online to visualize your Taskflow graph.
```cpp
tf::Taskflow taskflow;
tf::Task A = taskflow.emplace([] () {}).name("A");
tf::Task B = taskflow.emplace([] () {}).name("B");
tf::Task C = taskflow.emplace([] () {}).name("C");
tf::Task D = taskflow.emplace([] () {}).name("D");
tf::Task E = taskflow.emplace([] () {}).name("E");
A.precede(B, C, E);
C.precede(D);
B.precede(D, E);
taskflow.dump(std::cout); // dump the graph in DOT to std::cout
```
When you have tasks that are created at runtime (e.g., subflow, cudaFlow),
you need to execute the graph first to spawn these tasks
and dump the entire graph.
```cpp
tf::Executor executor;
tf::Taskflow taskflow;
tf::Task A = taskflow.emplace([](){}).name("A");
// create a subflow of two tasks B1->B2
tf::Task B = taskflow.emplace([] (tf::Subflow& subflow) {
tf::Task B1 = subflow.emplace([](){}).name("B1");
tf::Task B2 = subflow.emplace([](){}).name("B2");
B1.precede(B2);
}).name("B");
A.precede(B);
executor.run(tf).wait(); // run the taskflow to spawn subflows
tf.dump(std::cout); // dump the graph including dynamic tasks
```
# Monitor Thread Activities
Understanding thread activities is very important for performance analysis.
Cpp-Taskflow provides a default *observer* of type `tf::ExecutorObserver`
that lets users observe when a thread starts or stops participating in task scheduling.
```cpp
auto observer = executor.make_observer();
```
When you are running a task dependency graph,
the observer will automatically record the start and end timestamps of each executed task.
You can dump the entire execution timelines into a JSON file.
```cpp
executor.run(taskflow1); // run a task dependency graph 1
executor.run(taskflow2); // run a task dependency graph 2
executor.wait_for_all(); // block until all tasks finish
std::ofstream ofs("timestamps.json");
observer->dump(ofs); // dump the timeline to a JSON file
```
You can open the chrome browser to visualize the execution timelines through the
[chrome://tracing][ChromeTracing] developer tool.
In the tracing view, click the `Load` button to read the JSON file.
You shall see the tracing graph.
Each task is given a name of `i_j` where `i` is the thread id and `j` is the task number.
You can pan or zoom in/out the timeline to get into a detailed view.
# API Reference
The official [documentation][wiki] explains a complete list of
Cpp-Taskflow API.
Here, we highlight commonly used methods.
## Taskflow API
The class `tf::Taskflow` is the main place to create a task dependency graph.
The table below summarizes a list of commonly used methods.
| Method | Argument | Return | Description |
| -------- | --------- | ------- | ----------- |
| emplace | callables | tasks | creates a task with a given callable(s) |
| placeholder | none | task | inserts a node without any work; work can be assigned later |
| parallel_for | beg, end, callable, chunk | task pair | concurrently applies the callable chunk by chunk to the result of dereferencing every iterator in the range |
| parallel_for | beg, end, step, callable, chunk | task pair | concurrently applies the callable chunk by chunk to an index-based range with a step size |
| num_workers | none | size | queries the number of working threads in the pool |
| dump | ostream | none | dumps the taskflow to an output stream in GraphViz format |
### *emplace/placeholder*
You can use `emplace` to create a task from a target callable.
```cpp
tf::Task task = tf.emplace([] () { std::cout << "my task\n"; });
```
When a task cannot be determined beforehand, you can create a placeholder and assign the callable later.
```cpp
tf::Task A = tf.emplace([](){});
tf::Task B = tf.placeholder();
A.precede(B);
B.work([](){ /* do something */ });
```
### *parallel_for*
The method `parallel_for` creates a subgraph that applies the callable to each item in the given range of a container.
```cpp
auto v = {'A', 'B', 'C', 'D'};
auto [S, T] = tf.parallel_for(
v.begin(), // iterator to the beginning
v.end(), // iterator to the end
[] (int i) {
std::cout << "parallel " << i << '\n';
}
);
// add dependencies via S and T.
```
You can specify a *chunk* size (default one) in the last argument to force a task to include a certain number of items.
```cpp
auto v = {'A', 'B', 'C', 'D'};
auto [S, T] = tf.parallel_for(
v.begin(), // iterator to the beginning
v.end(), // iterator to the end
[] (int i) {
std::cout << "AB and CD run in parallel" << '\n';
},
2 // at least two items at a time
);
```
In addition to iterator-based construction,
`parallel_for` has another overload of index-based loop.
The first three argument of this overload indicates
starting index, ending index (exclusive), and step size.
```cpp
// [0, 11) with a step size of 2
auto [S, T] = tf.parallel_for(
0, 11, 2,
[] (int i) {
std::cout << "parallel_for on index " << i << std::endl;
},
2 // at least two items at a time
);
// will print 0, 2, 4, 6, 8, 10 (three partitions, {0, 2}, {4, 6}, {8, 10})
```
## Task API
Each time you create a task, the taskflow object adds a node to the present task dependency graph
and return a *task handle* to you.
A task handle is a lightweight object that defines a set of methods for users to
access and modify the attributes of the associated task.
The table below summarizes a list of commonly used methods.
| Method | Argument | Return | Description |
| -------------- | ----------- | ------ | ----------- |
| name | string | self | assigns a human-readable name to the task |
| work | callable | self | assigns a work of a callable object to the task |
| precede | task list | self | enables this task to run *before* the given tasks |
| succeed | task list | self | enables this task to run *after* the given tasks |
| num_dependents | none | size | returns the number of dependents (inputs) of this task |
| num_successors | none | size | returns the number of successors (outputs) of this task |
| empty | none | bool | returns true if the task points to a graph node or false otherwise |
| has_work | none | bool | returns true if the task points to a graph node with a callable assigned |
### *name*
The method `name` lets you assign a human-readable string to a task.
```cpp
A.name("my name is A");
```
### *work*
The method `work` lets you assign a callable to a task.
```cpp
A.work([] () { std::cout << "hello world!"; });
```
### *precede*
The method `precede` lets you add a preceding link from self to other tasks.
```cpp
// A runs before B, C, D, and E
A.precede(B, C, D, E);
```
The method `succeed` is similar to `precede` but operates in the opposite direction.
### *empty/has_work*
A task is empty is it is not associated with any graph node.
```cpp
tf::Task task; // assert(task.empty());
```
A placeholder task is associated with a graph node but has no work assigned yet.
```
tf::Task task = taskflow.placeholder(); // assert(!task.has_work());
```
## Executor API
The class `tf::Executor` is used for execution of one or multiple taskflow objects.
The table below summarizes a list of commonly used methods.
| Method | Argument | Return | Description |
| --------- | -------------- | ------------- | ------------------------ |
| run | taskflow | future | runs the taskflow once |
| run_n | taskflow, N | future | runs the taskflow N times |
| run_until | taskflow, binary predicate | future | keeps running the taskflow until the predicate becomes true |
| wait_for_all | none | none | blocks until all running tasks finish |
| make_observer | arguments to forward to user-derived constructor | pointer to the observer | creates an observer to monitor the thread activities of the executor |
### *run/run_n/run_until*
The run series are non-blocking call to execute a taskflow graph.
Issuing multiple runs on the same taskflow will automatically synchronize
to a sequential chain of executions.
```cpp
executor.run(taskflow); // runs a graph once
executor.run_n(taskflow, 5); // runs a graph five times
executor.run_until(taskflow, my_pred); // keeps running until the my_pred becomes true
executor.wait_for_all(); // blocks until all tasks finish
```
The first run finishes before the second run, and the second run finishes before the third run.
# System Requirements
To use the latest [Cpp-Taskflow](https://github.com/cpp-taskflow/cpp-taskflow/archive/master.zip), you only need a [C++14][C++14] compiler.
+ GNU C++ Compiler at least v5.0 with -std=c++14
+ Clang C++ Compiler at least v4.0 with -std=c++14
+ Microsoft Visual Studio at least v15.7 (MSVC++ 19.14); see [vcpkg guide](https://github.com/cpp-taskflow/cpp-taskflow/issues/143)
+ AppleClang Xode Version at least v8
+ Nvidia CUDA Toolkit and Compiler ([nvcc][nvcc]) at least v10.0 with -std=c++14
See the [C++ compiler support](https://en.cppreference.com/w/cpp/compiler_support) status.
# Compile Unit Tests, Examples, and Benchmarks
Cpp-Taskflow uses [CMake](https://cmake.org/) to build examples and unit tests.
We recommend using out-of-source build.
```bash
~$ cmake --version # must be at least 3.9 or higher
~$ mkdir build
~$ cd build
~$ cmake ../
~$ make & make test # run all unit tests
```
## Examples
The folder `examples/` contains several examples and is a great place to learn to use Cpp-Taskflow.
| Example | Description |
| ------- | ----------- |
| [simple.cpp](./examples/simple.cpp) | uses basic task building blocks to create a trivial taskflow graph |
| [debug.cpp](./examples/debug.cpp)| inspects a taskflow through the dump method |
| [parallel_for.cpp](./examples/parallel_for.cpp)| parallelizes a for loop with unbalanced workload |
| [subflow.cpp](./examples/subflow.cpp)| demonstrates how to create a subflow graph that spawns three dynamic tasks |
| [run_variants.cpp](./examples/run_variants.cpp)| shows multiple ways to run a taskflow graph |
| [composition.cpp](./examples/composition.cpp)| demonstrates the decomposable interface of taskflow |
| [observer.cpp](./examples/observer.cpp)| demonstrates how to monitor the thread activities in scheduling and running tasks |
| [condition.cpp](./examples/condition.cpp) | creates a conditional tasking graph with a feedback loop control flow |
## Benchmarks
Please visit [benchmarks](benchmarks/benchmarks.md) to learn to
compile the benchmarks.
# Who is Using Cpp-Taskflow?
Cpp-Taskflow is being used in both industry and academic projects to scale up existing workloads
that incorporate complex task dependencies.
- [OpenTimer][OpenTimer]: A High-performance Timing Analysis Tool for Very Large Scale Integration (VLSI) Systems
- [DtCraft][DtCraft]: A General-purpose Distributed Programming Systems using Data-parallel Streams
- [Firestorm][Firestorm]: Fighting Game Engine with Asynchronous Resource Loaders (developed by [ForgeMistress][ForgeMistress])
- [Shiva][Shiva]: An extensible engine via an entity component system through scripts, DLLs, and header-only (C++)
- [PID Framework][PID Framework]: A Global Development Methodology Supported by a CMake API and Dedicated C++ Projects
- [NovusCore][NovusCore]: An emulating project for World of Warraft (Wrath of the Lich King 3.3.5a 12340 client build)
- [SA-PCB][SA-PCB]: Annealing-based Printed Circuit Board (PCB) Placement Tool
- [LPMP](https://github.com/LPMP/LPMP): A C++ framework for developing scalable Lagrangian decomposition solvers for discrete optimization problems
- [Heteroflow](https://github.com/Heteroflow/Heteroflow): A Modern C++ Parallel CPU-GPU Task Programming Library
- [OpenPhySyn](https://github.com/The-OpenROAD-Project/OpenPhySyn): A plugin-based physical synthesis optimization kit as part of the OpenRoad flow
[More...](https://github.com/search?q=cpp-taskflow&type=Code)
# Contributors
Cpp-Taskflow is being actively developed and contributed by the
[these people](https://github.com/cpp-taskflow/cpp-taskflow/graphs/contributors).
Meanwhile, we appreciate the support from many organizations for our developments.
| [
][UofU] | [
][UIUC] | [
][CSL] | [
][NSF] | [
][DARPA IDEA] |
| :---: | :---: | :---: | :---: | :---: |
# License
Cpp-Taskflow is licensed under the [MIT License](./LICENSE).
* * *
[Tsung-Wei Huang]: https://tsung-wei-huang.github.io/
[Chun-Xun Lin]: https://github.com/clin99
[Martin Wong]: https://ece.illinois.edu/directory/profile/mdfwong
[Andreas Olofsson]: https://github.com/aolofsson
[Gitter]: https://gitter.im/cpp-taskflow/Lobby
[Gitter badge]: ./image/gitter_badge.svg
[GitHub releases]: https://github.com/coo-taskflow/cpp-taskflow/releases
[GitHub issues]: https://github.com/cpp-taskflow/cpp-taskflow/issues
[GitHub insights]: https://github.com/cpp-taskflow/cpp-taskflow/pulse
[GitHub pull requests]: https://github.com/cpp-taskflow/cpp-taskflow/pulls
[GitHub contributors]: https://github.com/cpp-taskflow/cpp-taskflow/graphs/contributors
[GraphViz]: https://www.graphviz.org/
[AwesomeGraphViz]: https://dreampuf.github.io/GraphvizOnline/
[OpenMP Tasking]: https://www.openmp.org/spec-html/5.0/openmpsu99.html
[TBB FlowGraph]: https://www.threadingbuildingblocks.org/tutorial-intel-tbb-flow-graph
[OpenTimer]: https://github.com/OpenTimer/OpenTimer
[DtCraft]: https://github.com/tsung-wei-huang/DtCraft
[totalgee]: https://github.com/totalgee
[damienhocking]: https://github.com/damienhocking
[ForgeMistress]: https://github.com/ForgeMistress
[Patrik Huber]: https://github.com/patrikhuber
[DARPA IDEA]: https://www.darpa.mil/news-events/2017-09-13
[KingDuckZ]: https://github.com/KingDuckZ
[NSF]: https://www.nsf.gov/
[UIUC]: https://illinois.edu/
[CSL]: https://csl.illinois.edu/
[UofU]: https://www.utah.edu/
[wiki]: https://cpp-taskflow.github.io/cpp-taskflow/index.html
[release notes]: https://cpp-taskflow.github.io/cpp-taskflow/Releases.html
[PayMe]: https://www.paypal.me/twhuang/10
[C++17]: https://en.wikipedia.org/wiki/C%2B%2B17
[C++14]: https://en.wikipedia.org/wiki/C%2B%2B14
[email me]: mailto:twh760812@gmail.com
[Cpp Conference 2018]: https://github.com/CppCon/CppCon2018
[ChromeTracing]: https://www.chromium.org/developers/how-tos/trace-event-profiling-tool
[IPDPS19]: https://tsung-wei-huang.github.io/papers/ipdps19.pdf
[WorkStealing Wiki]: https://en.wikipedia.org/wiki/Work_stealing
[std::invoke]: https://en.cppreference.com/w/cpp/utility/functional/invoke
[std::future]: https://en.cppreference.com/w/cpp/thread/future
[cuda-zone]: https://developer.nvidia.com/cuda-zone
[nvcc]: https://developer.nvidia.com/cuda-llvm-compiler
[cuda-toolkit]: https://developer.nvidia.com/cuda-toolkit
[cudaGraph]: https://docs.nvidia.com/cuda/cuda-runtime-api/group__CUDART__GRAPH.html
[Firestorm]: https://github.com/ForgeMistress/Firestorm
[Shiva]: https://shiva.gitbook.io/project/shiva
[PID Framework]: http://pid.lirmm.net/pid-framework/index.html
[NovusCore]: https://github.com/novuscore/NovusCore
[SA-PCB]: https://github.com/choltz95/SA-PCB
[Presentation]: https://cpp-taskflow.github.io/
[chrome://tracing]: chrome://tracing