C++编程 –安全并发访问容器元素

C++ 安全并发访问容器元素

2014-9-24 flyfish
标准库STL的vector, deque, list等等不是线程安全的
例如 线程1正在使用迭代器（iterator）读vector
线程2正在对该vector进行插入操作，使vector重新分配内存，这样就造成线程1中的迭代器失效

STL的容器
多个线程读是安全的，在读的过程中，不能对容器有任何写入操作
多个线程可以同时对不同的容器做写入操作。
不能指望任何STL实现来解决线程难题，必须手动做同步控制.

方案1 对vector进行加锁处理

effective STL给出的Lock框架

template //一个为容器获取和释放互斥体的模板  
class Lock
{ //框架；其中的很多细节被省略了  
public:
	Lock(const Container& container) :c(container) 
	{  
		getMutexFor(c); 
		//在构造函数中获取互斥体 
	} 
	~Lock() 
	{   
		releaseMutexFor(c); 
		//在析构函数中释放它
	} 
private: const Container& c; 
};  

如果需要实现工业强度，需要做更多的工作。


方案2 微软的Parallel Patterns Library (PPL)


看MSDN
PPL 提供的功能

1 Task Parallelism: a mechanism to execute several work items (tasks) in parallel
任务并行：一种并行执行若干工作项（任务）的机制

2 Parallel algorithms: generic algorithms that act on collections of data in parallel
并行算法：并行作用于数据集合的泛型算法

3 Parallel containers and objects: generic container types that provide safe concurrent access to their elements
并行容器和对象：提供对其元素的安全并发访问的泛型容器类型


示例是对斐波那契数列（Fibonacci）的顺序计算和并行计算的比较

顺序计算是
使用 STL std::for_each 算法
结果存储在 std::vector 对象中。

并行计算是
使用 PPL Concurrency::parallel_for_each 算法
结果存储在 Concurrency::concurrent_vector 对象中。

// parallel-fibonacci.cpp
// compile with: /EHsc
#include 
#include 
#include 
#include 
#include 
#include 
#include 
#include 


using namespace Concurrency;
using namespace std;


// Calls the provided work function and returns the number of milliseconds 
// that it takes to call that function.
template 
__int64 time_call(Function&& f)
{
   __int64 begin = GetTickCount();
   f();
   return GetTickCount() - begin;
}


// Computes the nth Fibonacci number.
int fibonacci(int n)
{
   if(n < 2)
      return n;
   return fibonacci(n-1) + fibonacci(n-2);
}


int wmain()
{
   __int64 elapsed;


   // An array of Fibonacci numbers to compute.
   array a = { 24, 26, 41, 42 };


   // The results of the serial computation.
   vector> results1;


   // The results of the parallel computation.
   concurrent_vector> results2;


   // Use the for_each algorithm to compute the results serially.
   elapsed = time_call([&] 
   {
      for_each (a.begin(), a.end(), [&](int n) {
         results1.push_back(make_tuple(n, fibonacci(n)));
      });
   });   
   wcout << L"serial time: " << elapsed << L" ms" << endl;


   // Use the parallel_for_each algorithm to perform the same task.
   elapsed = time_call([&] 
   {
      parallel_for_each (a.begin(), a.end(), [&](int n) {
         results2.push_back(make_tuple(n, fibonacci(n)));
      });


      // Because parallel_for_each acts concurrently, the results do not 
      // have a pre-determined order. Sort the concurrent_vector object
      // so that the results match the serial version.
      sort(results2.begin(), results2.end());
   });   
   wcout << L"parallel time: " << elapsed << L" ms" << endl << endl;


   // Print the results.
   for_each (results2.begin(), results2.end(), [](tuple& pair) {
      wcout << L"fib(" << get<0>(pair) << L"): " << get<1>(pair) << endl;
   });
}

命名空间Concurrency首字母大写，一般命名空间全是小写。

贴一个简单的示例代码
使用parallel_for_each 算法计算std::array 对象中每个元素的平方
参数分别是lambda 函数、函数对象和函数指针。

#include "stdafx.h"
#include 
#include 
#include 
using namespace Concurrency;
using namespace std;
using namespace std::tr1;


// Function object (functor) class that computes the square of its input.
template
class SquareFunctor
{
public:
	void operator()(Ty& n) const
	{
		n *= n;
	}
};


// Function that computes the square of its input.
template
void square_function(Ty& n)
{
	n *= n;
}
int _tmain(int argc, _TCHAR* argv[])
{
	// Create an array object that contains 5 values.
	array values = { 1, 2, 3, 4, 5 };


	// Use a lambda function, a function object, and a function pointer to 
	// compute the square of each element of the array in parallel.


	// Use a lambda function to square each element.
	parallel_for_each(values.begin(), values.end(), [](int& n){n *= n;});


	// Use a function object (functor) to square each element.
	parallel_for_each(values.begin(), values.end(), SquareFunctor());


	// Use a function pointer to square each element.
	parallel_for_each(values.begin(), values.end(), &square_function);


	// Print each element of the array to the console.
	for_each(values.begin(), values.end(), [](int& n) { 
		wcout << n << endl;
	});
	return 0;
}

在微软的concurrent_vector.h文件中有这样一句
Microsoft would like to acknowledge that this concurrency data structure implementation
is based on Intel implementation in its Threading Building Blocks ("Intel Material").
也就是微软的concurrent_vector是在Intel 的Threading Building Blocks基础上实现的。

方案3 Intel TBB(Threading Building Blocks)
 Intel TBB 提供的功能
 1 直接使用的线程安全容器，比如 concurrent_vector 和 concurrent_queue。
 2 通用的并行算法，如 parallel_for 和 parallel_reduce。 
 3 模板类 atomic 中提供了无锁（Lock-free或者mutex-free）并发编程支持。

方案4 无锁数据结构支持库Concurrent Data Structures (libcds). 
地址 http://sourceforge.net/projects/libcds/
下载以后里面直接有从VC2008到VC2013的编译环境，依赖于boost库

方案5 Boost 使用boost.lockfree

boost.lockfree实现了三种无锁数据结构：


1 boost::lockfree::queue
2 boost::lockfree::stack
3 boost::lockfree::spsc_queue

生产者-消费者
下面的代码实现的是
实现了一个多写生成，多消费 队列。
产生整数，并被4个线程消费

#include 
#include 
#include 


#include 


boost::atomic_int producer_count(0);
boost::atomic_int consumer_count(0);


boost::lockfree::queue queue(128);


const int iterations = 10000000;
const int producer_thread_count = 4;
const int consumer_thread_count = 4;


void producer(void)
{
    for (int i = 0; i != iterations; ++i) {
        int value = ++producer_count;
        while (!queue.push(value))
            ;
    }
}


boost::atomic done (false);
void consumer(void)
{
    int value;
    while (!done) {
        while (queue.pop(value))
            ++consumer_count;
    }


    while (queue.pop(value))
        ++consumer_count;
}


int main(int argc, char* argv[])
{
    using namespace std;
    cout << "boost::lockfree::queue is ";
    if (!queue.is_lock_free())
        cout << "not ";
    cout << "lockfree" << endl;


    boost::thread_group producer_threads, consumer_threads;


    for (int i = 0; i != producer_thread_count; ++i)
        producer_threads.create_thread(producer);


    for (int i = 0; i != consumer_thread_count; ++i)
        consumer_threads.create_thread(consumer);


    producer_threads.join_all();
    done = true;


    consumer_threads.join_all();


    cout << "produced " << producer_count << " objects." << endl;
    cout << "consumed " << consumer_count << " objects." << endl;
}