C++并发编程——在运行时选择线程数量
在编写多线程程序时，运行多少线程比较合适呢？线程并不是越多越好，理论上，硬件支持多少线程数，就开多少个线程比较合适，有的比如完成端口IOCP中建议开2倍线程数，因为考虑到有些线程可能会挂起等情况。但最重要的一条，首先要获取当前硬件支持的线程数，通常情况下为CPU核数。
std::thread::hardware_concurrency(); //获取当前CPU核心数量代码示例：
以下代码为std::accumulate的简单并行版本实现，通过将大量的累加操作，分配给多个线程去计算，最后将各个线程计算的结果累加，得出最终结果。真正的并行计算任务分割是很麻烦的，这里并不需要考虑线程的同步等问题。
template<typename Iterator,typename T>
struct accumulate_block
{
void operator()(Iterator first,Iterator last,T& result)
{
result = std::accumulate(first,last,result);
}
};
template<typename Iterator,typename T>
T parallel_accumulate(Iterator first,Iterator last,T init)
{
unsigned long const length=std::distance(first,last);
if(!length)
return init;
unsigned long const min_per_thread=25;
unsigned long const max_threads=(length+min_per_thread-1)/min_per_thread; //获取最大线程数量
unsigned long const hardware_threads=std::thread::hardware_concurrency(); //获取当前CPU核心数量
unsigned long const num_threads=std::min(hardware_threads!=0?hardware_threads:2,max_threads);//运行线程数量
unsigned long const block_size=length/num_threads;
std::vector1;++i)
{
Iterator block_end=block_start;
std::advance(block_end,block_size);
v_threads[i]=std::thread(accumulate_block(),block_start,block_end,std::ref(results[i]));
block_start=block_end;
}
accumulate_block()(block_start,last,results[num_threads-1]); //计算剩下的数,相当于在主线程中计算
std::for_each(v_threads.begin(),v_threads.end(),std::mem_fn(&std::thread::join));//等待所有线程计算完成
return std::accumulate(results.begin(),results.end(),init);
}
int _tmain(int argc, _TCHAR* argv[])
{
std::vector<int> v(100000);
std::iota(v.begin(),v.end(),1);
long long sum=parallel_accumulate(v.begin(),v.end(),0);
cout<<"sum="<return 0;
} 相关STL源码：
//std::distance源码
template<class _BidIt,
class _Diff> inline
void _Distance2(_BidIt _First, _BidIt _Last, _Diff& _Off,
bidirectional_iterator_tag)
{ // add to _Off distance between bidirectional iterators (redundant)
for (; _First != _Last; ++_First)
++_Off;
}
template<class _InIt> inline
typename iterator_traits<_InIt>::difference_type
distance(_InIt _First, _InIt _Last)
{ // return distance between iterators
typename iterator_traits<_InIt>::difference_type _Off = 0;
_Distance2(_First, _Last, _Off, _Iter_cat(_First));
return (_Off);
}//std::advance源代码
// TEMPLATE FUNCTION advance
template<class _InIt,
class _Diff> inline
void _Advance(_InIt& _Where, _Diff _Off, input_iterator_tag)
{ // increment iterator by offset, input iterators
#if _ITERATOR_DEBUG_LEVEL == 2
if (_Off < 0)
_DEBUG_ERROR("negative offset in advance");
#endif /* _ITERATOR_DEBUG_LEVEL == 2 */
for (; 0 < _Off; --_Off)
++_Where;
}
template<class _FwdIt,
class _Diff> inline
void _Advance(_FwdIt& _Where, _Diff _Off, forward_iterator_tag)
{ // increment iterator by offset, forward iterators
#if _ITERATOR_DEBUG_LEVEL == 2
if (_Off < 0)
_DEBUG_ERROR("negative offset in advance");
#endif /* _ITERATOR_DEBUG_LEVEL == 2 */
for (; 0 < _Off; --_Off)
++_Where;
}
template<class _BidIt,
class _Diff> inline
void _Advance(_BidIt& _Where, _Diff _Off, bidirectional_iterator_tag)
{ // increment iterator by offset, bidirectional iterators
for (; 0 < _Off; --_Off)
++_Where;
for (; _Off < 0; ++_Off)
--_Where;
}
template<class _RanIt,
class _Diff> inline
void _Advance(_RanIt& _Where, _Diff _Off, random_access_iterator_tag)
{ // increment iterator by offset, random-access iterators
_Where += _Off;
}
template<class _InIt,
class _Diff> inline
void advance(_InIt& _Where, _Diff _Off)
{ // increment iterator by offset, arbitrary iterators
_Advance(_Where, _Off, _Iter_cat(_Where));
}//获取硬件线程数量
static unsigned int hardware_concurrency() _NOEXCEPT
{ // return number of hardware thread contexts
return (::Concurrency::details::_GetConcurrency());
}