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 conc