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线程对象与线程的不同

2024-08-09 02:20:46   225人阅读

线程的handle是指向"线程核心对象"，而不是指向线程本身。

#define WIN32_LEAN_AND_MEAN

#include <stdlib.h>
#include <stdio.h>
#include <Windows.h>

DWORD WINAPI ThreadFunc(LPVOID);

int main()
{
	HANDLE hThrd;
	DWORD threadID;
	int i;

	for ( i = 0; i < 5; i++)
	{
		hThrd = CreateThread(NULL, 0, ThreadFunc, (LPVOID)i, 0, &threadID);
		if (hThrd)
		{
			printf("Thread launched %d\n",i);
		}
	}
	Sleep(2000);

	return EXIT_SUCCESS;
}


DWORD WINAPI ThreadFunc(LPVOID n)
{
	int i;
	for( i =0; i < 10; i++)
		printf("%d%d%d%d%d%d%d%d%d%d\n",n,n,n,n,n,n,n,n,n,n);
	return 0;
}


“线程核心对象”引用到的那个线程也会令核心对象开启。

“引用计数”机制保证新的进程有个地方可以写下其返回值。这样的机制也保证就线程能够读取那个返回值----只要它没有调用CloseHandle（）。

BOOL GetExitCodeThread（

           HANDLE hthread，
           LPDWORD lpExitCode
）;
#define WIN32_LEAN_AND_MEAN#include <stdlib.h>#include <stdio.h>#include <Windows.h>#include <conio.h>DWORD WINAPI ThreadFunc(LPVOID);int main(){<span style="white-space:pre">	</span>HANDLE hThrd1;<span style="white-space:pre">	</span>HANDLE hThrd2;<span style="white-space:pre">	</span>DWORD exitCode1 = 0;<span style="white-space:pre">	</span>DWORD exitCode2 = 0;<span style="white-space:pre">	</span>DWORD threadId;<span style="white-space:pre">	</span>hThrd1 = CreateThread(NULL,<span style="white-space:pre">		</span>0,<span style="white-space:pre">		</span>ThreadFunc,<span style="white-space:pre">		</span>(LPVOID)1,<span style="white-space:pre">		</span>0,<span style="white-space:pre">		</span>&threadId);<span style="white-space:pre">	</span>if (hThrd1)<span style="white-space:pre">	</span>{<span style="white-space:pre">		</span>printf(" Thread 1 lauched\n");<span style="white-space:pre">	</span>}<span style="white-space:pre">	</span>hThrd2 = CreateThread(NULL,<span style="white-space:pre">		</span>0,<span style="white-space:pre">		</span>ThreadFunc,<span style="white-space:pre">		</span>(LPVOID)2,<span style="white-space:pre">		</span>0,<span style="white-space:pre">		</span>&threadId);<span style="white-space:pre">	</span>if (hThrd2)<span style="white-space:pre">	</span>{<span style="white-space:pre">		</span>printf("Thread 2 launched\n");<span style="white-space:pre">	</span>}<span style="white-space:pre">	</span>for(;;)<span style="white-space:pre">	</span>{<span style="white-space:pre">		</span>printf(" Please any key to exit....\n");<span style="white-space:pre">		</span>getch();<span style="white-space:pre">		</span>GetExitCodeThread(hThrd1,&exitCode1);<span style="white-space:pre">		</span>GetExitCodeThread(hThrd2,&exitCode2);<span style="white-space:pre">		</span>if (exitCode1 == STILL_ACTIVE)<span style="white-space:pre">		</span>{<span style="white-space:pre">			</span>puts("Thread 1 is still running!\n");<span style="white-space:pre">		</span>}<span style="white-space:pre">		</span>if(exitCode2 == STILL_ACTIVE)<span style="white-space:pre">		</span>{<span style="white-space:pre">			</span>puts("Thread2 is still running!\n");<span style="white-space:pre">		</span>}<span style="white-space:pre">		</span>if (exitCode1 != STILL_ACTIVE && exitCode2 != STILL_ACTIVE)<span style="white-space:pre">			</span>break;<span style="white-space:pre">	</span>}<span style="white-space:pre">	</span>CloseHandle(hThrd1);<span style="white-space:pre">	</span>CloseHandle(hThrd2);<span style="white-space:pre">	</span>printf("Thread 1 returned %d\n",exitCode1);<span style="white-space:pre">	</span>printf("Thread 2 returned %d \n",exitCode2);<span style="white-space:pre">	</span>return EXIT_SUCCESS;}DWORD WINAPI ThreadFunc(LPVOID n){<span style="white-space:pre">	</span>Sleep((DWORD)n*1000*2);<span style="white-space:pre">	</span>return (DWORD)n*10;}
每次运行的结果都不一样，这中不确定性在多线程中是非常常见的
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n9TnGX5SRbPDfJ4DdkqhjBHMrtv9bFfxhCAAAQgsFwE5nKlroZSlbqajm0qsi2TtdHJZpm3dbl1jbuOcZ2uzxi6xow9BCAAAQj0S2AuTb0pZDUdba19mSzo1Yyk19jOXeZ9xbvo+LX+MrMiNghAAAIQaEdgLrff24VSbxWaoBphvWWe2mn5TDs/T+pkDQEIQGC5CGx5/MT6pTik8B90afO33+Mma6/6rE5yycJY+1pbNhqHbZON1Vuf8tWkt2vF+3au1cm3lVXtN/mw+knit/NDDDY2q5Ncsnis+CXXWPbyrXFsJ3u2EIAABCCwWAJTNfXFhs7qEIAABCAAAQhYAitz+90GzT4EIAABCEAAAuMEaOrjTJBAAAIQgAAEVpIATX0ly0bQEIAABCAAgXECNPVxJkggAAEIQAACK0mApr6SZSNoCEAAAhCAwDgBmvo4EyQQgAAEIACBlSRQ+Rfl9J3kOCt9R9nqJYtt5zWeNBY7L8TaNY9p58+Sj42ta15xXH36in0zhgAEIACB/gjUXqnbZmD3w/LxuL+QunuaJBY1qjBX8yVrG4Gd23bOvOyUUx/r9emrj3jwAQEIQAAC5QQqm3rZibxMVu4WKQQgAAEIQAAC8yZQefu9ayC6ylXj1zj4CTKNpQ9yycJ++FhdGHfVhzltP/Fabed1sauLXzrFUTW268lWMs3RONZLHraytTaSyc7q7Bzp2UIAAhCAwHITqLxS7xJ2aA5qCGoUGsd+pNc22MlWMs2RrkwvW9loziRb62uS+VVzFFtZ/FVzYrl8BLnitPtVeusnzItjkK+q+WV665N9CEAAAhBYPgK9XKmrYVSlp6aiRmHtymTS1+lkM+1WazTlMMk68j3J3K5z6uKv080zxq45YQ8BCEAAAt0I9NLUm5ZUU9HW2pfJgl7NRnqN7dxp9+VTa0zrz86PfWtsbZZhfxa5L0NexAABCEAgRwK93H7vA1xoevNsfFpLTW3e6/fBzPqYNv5p59tY2IcABCAAgcUQqP1Pr6rxKTQ1wDC2OsklC2Ptl82VrMnG6q3PeL3gr0yvdcq21rfVy7eVVe03+bD6svjq9NIpnngcYpJM8ck21kku+3hcNr/MRzxf89hCAAIQgMByEKht6ssRIlFAAAIQgAAEINCGwNLcfm8TLDYQgAAEIAABCFQToKlXs0EDAQhAAAIQWCkCNPWVKhfBQgACEIAABKoJ0NSr2aCBAAQgAAEIrBQBmvpKlYtgIQABCEAAAtUEaOrVbNBAAAIQgAAEVorAxH9RTt9ZjrPVd6CDPLaxunge41EClh3cRtnEI7GCU0yGMQQgkBuBia/U7Qk07GusE2wAaeW5gZ02X/Gc1s+08209p/W1iPmrHv8imLEmBCCwugQmvlJf3ZSJvC2BVWmIVb8ArUr8beuBHQQgAIEmAgtt6vFJNz45z1rfBKdu/VgXfFXFL7nmaNy0fqyP52ssu9iv9JLHY80r28o26Oy+fMXyMLa6MG76WL/B1s6Xrk4mG61TZht01s7aaB5bCEAAAqkQmPj2uwUQTpo6cbY9aVp7zZFMvoNcP0Fm9dqfVK816rbyHbbhozWLwfD/ZFOlt7bT7GvtOJZZra91Qsxaw8psPJJL1iZP2cp3mCNZ2I99Sie5bOw4yPSx8rCvH+nZQgACEEiRQC9X6vYE2hWSTtZl8+p0Yc2gDz91J+ygn/QzzdxJ1yybpzjKOEtXNm8VZHXx2xqHXMryX4UciRECEIDAvAj00tSnCbbqRK2TvfQa27VincZlNlbWZl/ryafGbeb2bRNiCOuHH8WjNeKx5KuyXfX4V4UzcUIAAnkQ2LosaapptY3H2qsxBFnVx9pX2fQtt3EpNsm6rqV58hPPL8vPztE8yeL5TeMy/01zuujL/Mcxa9zFr2zL/Aed5FW+Z61XfGwhAAEI9EFg4v/0anwSLGsWsY0Clm2sl1x2Vh90GstOY9lLrnGTXnZVWzs/Xl86rRmPY59N+ir7IK9aQz41V3Ya261s62ysvfY1L4zjuVZXppePqm3dfOnsmrFM49h/2Zyq+KwPO08+Z63XOmwhAAEI9EFg4qbex+K5+FBjKGsa82Cw6PXnkSNrQAACEICAcwt/pp5qEdRIld+8G/qi11febCEAAQhAYH4EaOozYj3vJh6nsej143gYQwACEIDA7AkszYtys0+VFSAAAQhAAAJpE6Cpp11fsoMABCAAgYwI0NQzKjapQgACEIBA2gRo6mnXl+wgAAEIQCAjApUvysVvT4uJXsCyeslkM+9tVSxWHmKaNE75mXT+rHlUxSe51o/jb9Jr3rJvlUecXx9xz9J3H/HhAwIQgIAlUHulbk+Sdj84iMfW6bz3y2KxJ2PpJZt3fNOsN03MIW/lXhZDk75sTk4yy97u58SAXCEAgdUiUHmlXtYMymSrle5k0S4q77aNZFHxTUaz/1m5598/UTxCAAKrSqCyqXdNSA1IJ1iNg58g01j6IJcs7IeP1YVxV32Yo0/sS/Iu267rd12zzr/V2X27hpWHvKyuS56T2Nq1w7oaK4a2Y7u25gaZ5lfpy2ymmV+Wg12bfQhAAAKrQKD29nvbBMIJWCdUnYw1jn1Ir22wk61kmiNdmV62stGcsq21LdNXyep8W59l8VX5lLxpvnwGe8VhZVYun/PcxrFobeWlcdNWuQW7srl1equrWsfaWP/al96O5Svo9LH7krGFAAQgsGwEemnqTSe8cMKssgk6nVBjONJV6WP7srHmVq1fNqeNTP6sf8nmMb/NGvOyCQy65D6vuFgHAhCAQG4Eerv9XgdOJ3xtrW2ZLOhts7RjO7dpP/bRZN9Vr9gnXWfa+V3jnZW98tB2Vusswm+KOS2CI2tCAALzIdDLlXofoYbGqObYl7/gRyflWfhXvHaNtrHbeNrMt/Zt18CumoBlrjqWWcO9jAoyCEBgWQnU/qdX45OdToQhGauTXLIw1r4Sl43GsY8wjm2sD+tTdnV6q7Nraq6VVe03+Yj1XXyHNdvMtzaxf6uzOchuWr31Ge/HvrWmtbM2Qa9xvB/mWF2bsbUJ+/bTxr+1D/vx+tJXyaVnCwEIQGCZCNQ29WUKlFgg0CeBuFnH4z7XwhcEIACBeRFYmtvv80qYdSAQCISr+fAJzZyGXqDg/yAAgQQIzOVFuQQ4kUKCBNTYE0yNlCAAgUwJcKWeaeFJGwIQgAAE0iNAU0+vpmQEAQhAAAKZEqCpZ1p40oYABCAAgfQI0NTTqykZQQACEIBApgRo6pkWnrQhAAEIQCA9AjT19GpKRhCAAAQgkCkBmnqmhSdtCEAAAhBIjwBNPb2akhEEIAABCGRKgKaeaeFJGwIQgAAE0iNAU0+vpmQEAQhAAAKZEqCpZ1p40oYABCAAgfQI0NTTqykZQQACEIBApgRo6pkWnrQhAAEIQCA9AjT19GpKRhCAAAQgkCkBmnqmhSdtCEAAAhBIjwBNPb2akhEEIAABCGRKgKaeaeFJGwIQgAAE0iNAU0+vpmQEAQhAAAKZEqCpZ1p40oYABCAAgfQI0NTTqykZQQACEIBApgS2T5r38ePHS6eura1tyGMbq9swYqeUgGUHt1JEG0KxgtMGEnYgAIFMCUx8pW5PoGFfY51gA08rz5TvxGmL58QOeppo69mTy7m6WfX45wqLxSAAgZUnMPGV+spnTgKNBFalIVb9ArQq8TcWAgMIQAACLQkstKnHJ9345DxrfROjuvVjXfBVFb/kmqNx0/qxPp6vsexiv9JLHo81r2wr26Cz+/IVy8PY6sK46WP9Bls7X7o6mWy0Tplt0Fk7a6N5bCEAAQikQmDi2+8WQDhp6sTZ9qRp7TVHMvkOcv0EmdVrf1K91qjbynfYho/WLAbD/5NNld7aTrOvteNYZrW+1gkxaw0rs/FILlmbPGUr32GOZGE/9imd5LKx4yDTx8rDvn6kZwsBCEAgRQK9XKnbE2hXSDpZl82r04U1gz781J2wg37SzzRzJ12zbJ7iKOMsXdm8VZDVxW9rHHIpy38VciRGCEAAAvMi0EtTnybYqhO1TvbSa2zXinUal9lYWZt9rSefGreZ27dNiCGsH34Uj9aIx5KvynbV418VzsQJAQjkQWDrsqSpptU2HmuvxhBkVR9rX2XTt9zGpdgk67qW5slPPL8sPztH8ySL5zeNy/w3zemiL/Mfx6xxF7+yLfMfdJJX+Z61XvGxhQAEINAHgS2Pn1i/FDv67dtvum/ceyAWj4zjk2BZs4ht5EC2sV5y2Vl90GksO41lL7nGTXrZVW3t/Hh96bRmPI59Numr7IO8ag351FzZaWy3sq2zsfba17wwjudaXZlePqq2dfOls2vGMo1j/2VzquKzPuw8+Zy1XuuwhQAEINAHgYmbeh+L5+JDjaGsacyDwaLXn0eOrAEBCEAAAs4t/Jl6qkVQI1V+827oi15febOFAAQgAIH5EaCpz4j1vJt4nMai14/jYQwBCEAAArMnsDQvys0+VVaAAAQgAAEIpE2App52fckOAhCAAAQyIkBTz6jYpAoBCEAAAmkToKmnXV+ygwAEIACBjAjQ1DMqNqlCAAIQgEDaBGjqadeX7CAAAQhAICMCNPWMik2qEIAABCCQNgGaetr1JTsIQAACEMiIAE09o2KTKgQgAAEIpE2App52fckOAhCAAAQyIkBTz6jYpAoBCEAAAmkToKmnXV+ygwAEIACBjAjQ1DMqNqlCAAIQgEDaBGjqadeX7CAAAQhAICMCNPWMik2qEIAABCCQNgGaetr1JTsIQAACEMiIAE09o2KTKgQgAAEIpE2App52fckOAhCAAAQyIkBTz6jYpAoBCEAAAmkToKmnXV+ygwAEIACBjAjQ1DMqNqlCAAIQgEDaBGjqadeX7CAAAQhAICMCNPWMik2qEIAABCCQNgGaetr1JTsIQAACEMiIAE09o2KTKgQgAAEIpE2App52fckOAhCAAAQyIkBTz6jYpAoBCEAAAmkToKmnXV+ygwAEIACBjAjQ1DMqNqlCAAIQgEDaBGjqadeX7CAAAQhAICMCNPWMik2qEIAABCCQNgGaetr1JTsIQAACEMiIAE09o2KTKgQgAAEIpE2App52fckOAhCAAAQyIkBTz6jYpAoBCEAAAmkToKmnXV+ygwAEIACBjAjQ1DMqNqlCAAIQgEDaBGjqadeX7CAAAQhAICMCNPWMik2qEIAABCCQNgGaetr1JTsIQAACEMiIAE09o2KTKgQgAAEIpE2App52fckOAhCAAAQyIkBTz6jYpAoBCEAAAmkToKmnXV+ygwAEIACBjAjQ1DMqNqlCAAIQgEDaBGjqadeX7CAAAQhAICMCNPWMik2qEIAABCCQNgGaetr1JTsIQAACEMiIAE09o2KTKgQgAAEIpE2App52fckOAhCAAAQyIkBTz6jYpAoBCEAAAmkToKmnXV+ygwAEIACBjAjQ1DMqNqlCAAIQgEDaBGjqadeX7CAAAQhAICMCNPWMik2qEIAABCCQNgGaetr1JTsIQAACEMiIAE09o2KTKgQgAAEIpE2App52fckOAhCAAAQyIkBTz6jYpAoBCEAAAmkToKmnXV+ygwAEIACBjAjQ1DMqNqlCAAIQgEDaBGjqadeX7CAAAQhAICMCNPWMik2qEIAABCCQNgGaetr1JTsIQAACEMiIAE09o2KTKgQgAAEIpE2App52fckOAhCAAAQyIkBTz6jYpAoBCEAAAmkToKmnXV+ygwAEIACBjAjQ1DMqNqlCAAIQgEDaBGjqadeX7CAAAQhAICMCNPWMik2qEIAABCCQNgGaetr1JTsIQAACEMiIAE09o2KTKgQgAAEIpE2App52fckOAhCAAAQyIkBTz6jYpAoBCEAAAmkToKmnXV+ygwAEIACBjAjQ1DMqNqlCAAIQgEDaBGjqadeX7CAAAQhAICMCNPWMik2qEIAABCCQNgGaetr1JTsIQAACEMiIAE09o2KTKgQgAAEIpE2App52fckOAhCAAAQyIkBTz/3S55UAAAARSURBVKjYpAoBCEAAAmkT+H/G+/Hy9mOCwQAAAABJRU5ErkJggg==" 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" alt="" />
线程对象与线程的不同






























        

            
                
                  
                
                

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