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Hipe是采用C++11编写的高性能、跨平台、简单易用且功能强大的C++并发库。内置了多个独立线程池(Thread Pool),可针对多种场景提供高并发服务。(A high performance concurrent library)

License: Apache License 2.0

C++ 93.87% CMake 6.13%

hipe's Introduction

workspace

workspace是基于C++11的轻量级异步执行框架,支持:通用任务异步执行、优先级任务调度、自适应动态线程池、高效静态线程池、异常处理机制等。

目录

特点

  • 轻量的:Header-Only & 代码量 <= 1000行 & 接口简单。
  • 高效的:超轻量级任务支持异步顺序执行,提高了框架的并发性能。
  • 灵活的:支持多种任务类型、动态线程调整、可通过workspace构建不同的池模型。
  • 稳定的:利用std::function的小任务优化减少内存碎片、拥有良好的异步线程异常处理机制。
  • 兼容性:纯C++11实现,跨平台,且兼容C++11以上版本。

主要模块

workbranch

workbranch(工作分支)是动态线程池的抽象,内置了一条线程安全的任务队列用于同步任务。其管理的每一条异步工作线程被称为worker,负责从任务队列不断获取任务并执行。(以下示例按顺序置于workspace/example/

让我们先简单地提交一点任务,当你的任务带有返回值时,workbranch会返回一个std::future,否则返回void。

#include <workspace/workspace.h>

int main() {
    // 2 threads
    wsp::workbranch br(2);
    // return void
    br.submit([]{ std::cout<<"hello world"<<std::endl; });  
    // return std::future<int>
    auto result = br.submit([]{ return 2023; });  
    std::cout<<"Got "<<result.get()<<std::endl;   
    // wait for tasks done (timeout: 1000 milliseconds)
    br.wait_tasks(1000); 
}

由于返回一个std::future会带来一定的开销,如果你不需要返回值并且希望程序跑得更快,那么你的任务应该是void()类型的。

当你有一个任务并且你希望它能尽快被执行时,你可以指定该任务的类型为urgent,如下:

#include <workspace/workspace.h>

int main() {
    // 1 threads
    wsp::workbranch br;
    br.submit<wsp::task::nor>([]{ std::cout<<"task B done\n";}); // normal task 
    br.submit<wsp::task::urg>([]{ std::cout<<"task A done\n";}); // urgent task
    br.wait_tasks(); // wait for tasks done (timeout: no limit)
}

在这里我们通过指定任务类型为wsp::task::urg,来提高任务的优先级。最终 在我的机器上:

jack@xxx:~/workspace/example/build$ ./e2
task A done
task B done

在这里我们不能保证task A一定会被先执行,因为当我们提交task A的时候,task B可能已经在执行中了。urgent标签可以让任务被插入到队列头部,但无法改变已经在执行的任务。

假如你有几个轻量异步任务,执行他们只需要非常短暂的时间。同时,按照顺序执行它们对你来说没有影响,甚至正中你下怀。那么你可以把任务类型指定为sequence,以便提交一个任务序列。这个任务序列会被单个线程顺序执行:

#include <workspace/workspace.h>

int main() {
    wsp::workbranch br;
    // sequence tasks
    br.submit<wsp::task::seq>([]{std::cout<<"task 1 done\n";},
                              []{std::cout<<"task 2 done\n";},
                              []{std::cout<<"task 3 done\n";},
                              []{std::cout<<"task 4 done\n";});
    // wait for tasks done (timeout: no limit)
    br.wait_tasks();
}

任务序列会被打包成一个较大的任务,以此来减轻框架同步任务的负担,提高整体的并发性能。

当任务中抛出了一个异常,workbranch有两种处理方式:A-将其捕获并输出到终端 B-将其捕获并通过std::future传递到主线程。第二种需要你提交一个带返回值的任务。

#include <workspace/workspace.h>
// self-defined
class excep: public std::exception {
    const char* err;
public:
    excep(const char* err): err(err) {}
    const char* what() const noexcept override {
        return err;
    }
}; 
int main() {
    wsp::workbranch wbr;
    wbr.submit([]{ throw std::logic_error("A logic error"); });     // log error
    wbr.submit([]{ throw std::runtime_error("A runtime error"); }); // log error
    wbr.submit([]{ throw excep("XXXX");});                          // log error

    auto future1 =  wbr.submit([]{ throw std::bad_alloc(); return 1; }); // catch error
    auto future2 =  wbr.submit([]{ throw excep("YYYY"); return 2; });    // catch error
    try {
        future1.get();
    } catch (std::exception& e) {
        std::cerr<<"Caught error: "<<e.what()<<std::endl;
    }
    try {
        future2.get();
    } catch (std::exception& e) {
        std::cerr<<"Caught error: "<<e.what()<<std::endl;
    }
}

在我的机器上:

jack@xxx:~/workspace/test/build$ ./test_exception 
workspace: worker[140509071521536] caught exception:
  what(): A logic error
workspace: worker[140509071521536] caught exception:
  what(): A runtime error
workspace: worker[140509071521536] caught exception:
  what(): XXXX
Caught error: std::bad_alloc
Caught error: YYYY

supervisor

supervisor是异步管理者线程的抽象,负责监控workbranch的负载情况并进行动态调整。它允许你在每一次调控workbranch之后执行一个小任务,你可以用来写日志或者做一些其它调控等。

每一个supervisor可以管理多个workbranch。此时workbranch之间共享supervisor的所有设定。

#include <workspace/workspace.h>

int main() {
    wsp::workbranch br1(2);
    wsp::workbranch br2(2);

    // 2 <= thread number <= 4 
    // time interval: 1000 ms 
    wsp::supervisor sp(2, 4, 1000);

    sp.set_tick_cb([&br1, &br2]{
        auto now = std::chrono::system_clock::now();
        std::time_t timestamp = std::chrono::system_clock::to_time_t(now);
        std::tm local_time = *std::localtime(&timestamp);
        static char buffer[40];
        std::strftime(buffer, sizeof(buffer), "%Y-%m-%d %H:%M:%S", &local_time);
        std::cout<<"["<<buffer<<"] "<<"br1: [workers] "<<br1.num_workers()<<" | [blocking-tasks] "<<br1.num_tasks()<<'\n';
        std::cout<<"["<<buffer<<"] "<<"br2: [workers] "<<br2.num_workers()<<" | [blocking-tasks] "<<br2.num_tasks()<<'\n';
    });

    sp.supervise(br1);  // start supervising
    sp.supervise(br2);  // start supervising

    for (int i = 0; i < 1000; ++i) {
        br1.submit([]{std::this_thread::sleep_for(std::chrono::milliseconds(10));});
        br2.submit([]{std::this_thread::sleep_for(std::chrono::milliseconds(20));});
    }

    br1.wait_tasks();
    br2.wait_tasks();
}

在我的机器上,输出如下:

jack@xxx:~/workspace/example/build$ ./e4
[2023-06-13 12:24:31] br1: [workers] 4 | [blocking-tasks] 606
[2023-06-13 12:24:31] br2: [workers] 4 | [blocking-tasks] 800
[2023-06-13 12:24:32] br1: [workers] 4 | [blocking-tasks] 213
[2023-06-13 12:24:32] br2: [workers] 4 | [blocking-tasks] 600
[2023-06-13 12:24:33] br1: [workers] 4 | [blocking-tasks] 0
[2023-06-13 12:24:33] br2: [workers] 4 | [blocking-tasks] 404
[2023-06-13 12:24:34] br1: [workers] 3 | [blocking-tasks] 0
[2023-06-13 12:24:34] br2: [workers] 4 | [blocking-tasks] 204
[2023-06-13 12:24:35] br1: [workers] 2 | [blocking-tasks] 0
[2023-06-13 12:24:35] br2: [workers] 4 | [blocking-tasks] 4
[2023-06-13 12:24:35] br1: [workers] 2 | [blocking-tasks] 0
[2023-06-13 12:24:35] br2: [workers] 4 | [blocking-tasks] 0

workspace

workspace是一个托管器/任务分发器,你可以将workbranch和supervisor托管给它,并用workspace分配的组件专属ID来访问它们。将组件托管至workspace至少有以下几点好处:

  • 堆内存正确释放:workspace在内部用unique指针来管理组件,确保没有内存泄漏
  • 分支间任务负载均衡:workspace支持任务分发,在workbranch之间实现了简单高效的负载均衡
  • 避免空悬指针问题:当workbranch先于supervisor析构会造成空悬指针的问题,使用workspace可以避免这种情况
  • 更低的框架开销:workspace的任务分发机制能减少与工作线程的竞争,提高性能(见下Benchmark)。

我们可以通过workspace自带的任务分发机制来异步执行任务(调用submit)。

#include <workspace/workspace.h>

int main() {
    wsp::workspace spc;
    auto bid1 = spc.attach(new wsp::workbranch);
    auto bid2 = spc.attach(new wsp::workbranch);
    auto sid1 = spc.attach(new wsp::supervisor(2, 4));
    auto sid2 = spc.attach(new wsp::supervisor(2, 4));
    spc[sid1].supervise(spc[bid1]);  // start supervising
    spc[sid2].supervise(spc[bid2]);  // start supervising

    // Automatic assignment
    spc.submit([]{std::cout<<std::this_thread::get_id()<<" executed task"<<std::endl;});
    spc.submit([]{std::cout<<std::this_thread::get_id()<<" executed task"<<std::endl;});

    spc.for_each([](wsp::workbranch& each){each.wait_tasks();});
}

当我们需要等待任务执行完毕的时候,我们可以调用for_each+wait_tasks,并为每一个workbranch指定等待时间,单位是毫秒。

(更多详细接口见workspace/test/

辅助模块

futures

wsp::futures是一个std::future收集器(collector),可以缓存同类型的std::future,并进行批量操作。一个简单的操作如下:

#include <workspace/workspace.h>

int main() {
    wsp::futures<int> futures;
    wsp::workspace spc;
    spc.attach(new wsp::workbranch("br", 2));
    
    futures.add_back(spc.submit([]{return 1;}));
    futures.add_back(spc.submit([]{return 2;}));

    futures.wait();
    auto res = futures.get();
    for (auto& each: res) {
        std::cout<<"got "<<each<<std::endl;
    }
}

这里futures.get()返回的是一个std::vector<int>,里面保存了所有任务的返回值。

benchmark

空跑测试

测试原理:通过快速提交大量的空任务以考察框架同步任务的开销。
测试环境:Ubuntu20.04 : 16核 : AMD Ryzen 7 5800H with Radeon Graphics 3.20 GHz

<测试1>
在测试1中我们调用了submit<wsp::task::seq>,每次打包10个空任务并提交到workbranch中执行。结果如下:(代码见workspace/benchmark/bench1.cc

threads: 1  |  tasks: 100000000  |  time-cost: 2.68801 (s)
threads: 2  |  tasks: 100000000  |  time-cost: 3.53964 (s)
threads: 3  |  tasks: 100000000  |  time-cost: 3.99903 (s)
threads: 4  |  tasks: 100000000  |  time-cost: 5.26045 (s)
threads: 5  |  tasks: 100000000  |  time-cost: 6.65157 (s)
threads: 6  |  tasks: 100000000  |  time-cost: 8.40907 (s)
threads: 7  |  tasks: 100000000  |  time-cost: 10.5967 (s)
threads: 8  |  tasks: 100000000  |  time-cost: 13.2523 (s)

<测试2>
在测试2中我们同样将10个任务打成一包,但是是将任务提交到workspace中,利用workspace进行任务分发,且在workspace托管的workbranch只拥有 1条 线程。结果如下:(代码见workspace/benchmark/bench2.cc

threads: 1  |  tasks: 100000000  |  time-cost: 4.38221 (s)
threads: 2  |  tasks: 100000000  |  time-cost: 4.01103 (s)
threads: 3  |  tasks: 100000000  |  time-cost: 3.6797 (s)
threads: 4  |  tasks: 100000000  |  time-cost: 3.39314 (s)
threads: 5  |  tasks: 100000000  |  time-cost: 3.03324 (s)
threads: 6  |  tasks: 100000000  |  time-cost: 3.16079 (s)
threads: 7  |  tasks: 100000000  |  time-cost: 3.04612 (s)
threads: 8  |  tasks: 100000000  |  time-cost: 3.11893 (s)

<测试3>
在测试3中我们同样将10个任务打成一包,并且将任务提交到workspace中,但是workspace管理的每个workbranch中都拥有 2条 线程。结果如下:(代码见workspace/benchmark/bench3.cc

threads: 2  |  tasks: 100000000  |  time-cost: 4.53911 (s)
threads: 4  |  tasks: 100000000  |  time-cost: 7.0178 (s)
threads: 6  |  tasks: 100000000  |  time-cost: 6.00101 (s)
threads: 8  |  tasks: 100000000  |  time-cost: 5.97501 (s)
threads: 10 |  tasks: 100000000  |  time-cost: 5.63834 (s)
threads: 12 |  tasks: 100000000  |  time-cost: 5.17316 (s)

总结:利用workspace进行任务分发,且workbranch线程数为1的情况下,整个任务同步框架是静态的,任务同步开销最小。当workbranch内的线程数越多,面对大量空任务时对任务队列的竞争越激烈,框架开销越大。(更加详尽的测试结果见bench.md,测试代码于workspace/bench

如何使用

生成doxygen文档

请提前安装doxygen

# 与workspace同级目录中(Linux)
doxygen ./doxygen.conf

生成的文档在workspace/docs/中,可以在浏览器中打开workspace/docs/html/index.html并查看接口。

简单使用

# 项目代码与workspace同级(Linux)
g++ -I workspace/include xxx.cc -lpthread && ./a.out

其它平台可能需要链接不同的线程库,且可执行文件后缀不同。

运行已有实例(以example为例)

# 在"workspace/example"中
cmake -B build 
cd build
make
./e1

安装到系统(支持Win/Linux/Mac)

# 在"workspace/"中
cmake -B build 
cd build
sudo make install

注意事项

雷区

  1. 不要在任务中操纵组件,如:submit([&br]{br.wait_tasks();}); 会阻塞线程
  2. 不要在回调中操纵组件,如:set_tick_cb([&sp]{sp.suspend();});
  3. 不要让workbranch先于supervisor析构(空悬指针问题)。

接口安全性

组件接口 是否线程安全
workspace
workbranch
supervisor
futures

时间单位

workspace有关时间的接口单位都是 -> 毫秒(ms)

其它

参考书目

《C++并发编程》

旧版信息

由于历史原因,Hipe源码已不复存在,诸位请向前看吧。

联系我

邮箱: [email protected]

hipe's People

Contributors

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