为了试验一下多核CPU上排序算法的效率，得比较单任务情况下和多任务并行排序算法的差距，因此选用快速排序算法来进行比较。
测试环境：双核CPU 2.66GHZ
单核CPU 2.4GHZ
以下是一个快速排序算法的源代码：
UINTSplit(void **ppData, UINTuStart, UINTuEnd,
COMPAREFUNCCompareFunc)
{
void *pSelData;
UINTuLow;
UINTuHigh;
uLow = uStart;
uHigh = uEnd;
pSelData = ppData[uLow];
while ( uLow < uHigh )
{
while ( (*CompareFunc)(ppData[uHigh], pSelData) > 0
&& uLow != uHigh )
{
--uHigh;
}
if ( uHigh != uLow )
{
ppData[uLow] = ppData[uHigh];
++uLow;
}
while ( (*CompareFunc)( ppData[uLow], pSelData ) < 0
&& uLow != uHigh )
{
++uLow;
}
if ( uLow != uHigh )
{
ppData[uHigh] = ppData[uLow];
--uHigh;
}
}
ppData[uLow] = pSelData;
returnuLow;
}
voidQuickSort(void **ppData, UINTuStart, UINTuEnd,
COMPAREFUNCCompareFunc)
{
UINTuMid = Split(ppData, uStart, uEnd, CompareFunc );
if ( uMid > uStart )
{
QuickSort(ppData, uStart, uMid - 1, CompareFunc);
}
if ( uEnd > uMid )
{
QuickSort(ppData, uMid + 1, uEnd, CompareFunc);
}
}
先测试一下这个快速排序算法排一百万个随机整数所花的时间：
voidTest_QuickSort(void)
{
UINTi;
UINTuCount = 1000000; //1000000个
srand(time(NULL));
void **pp = (void **)malloc(uCount * sizeof(void *));
for ( i = 0; i < uCount; i++ )
{
pp[i] = (void *)(rand() % uCount);
}
clock_tt1 = clock();
QuickSort(pp, 0, uCount-1, UIntCompare);
clock_tt2 = clock();
printf("QuickSort 1000000 Time %ld\n", t2-t1);
free(pp);
}
在双核CPU2.66GHZ机器上运行测试程序，打印出花费的时间约为469 ms
在单核CPU2.4GHZ机器上运行测试程序，打印出花费时间约为500ms
可见在双核CPU上运行单任务程序和单核CPU完全是一样的，效率没有任何提高。
下面再来把上面的快速排序程序变成并行的，一个简单的方法就是将要排序的区间分成相同的几个段，然后对每个段进行快速排序，排序完后再使用归并算法将排好的几个区间归并成一个排好序的表，我们先四个线程来进行排序，代码如下：
void ** Merge(void **ppData, UINTuStart, UINTuEnd,
void **ppData2, UINTuStart2, UINTuEnd2, COMPAREFUNCcfunc)
{
UINTi, j, k;
UINTu1, u2, v1,v2;
void **pp1;
void **pp2;
void **pp = (void **)malloc( (uEnd-uStart+1+uEnd2-uStart2+1) * sizeof(void *));
if ( pp == NULL )
{
returnNULL;
}
if ( (*cfunc)(ppData2[uStart2], ppData[uStart]) > 0 )
{
u1 = uStart;
u2 = uEnd;
v1 = uStart2;
v2 = uEnd2;
pp1 = ppData;
pp2 = ppData2;
}
else
{
u1 = uStart2;
u2 = uEnd2;
v1 = uStart;
v2 = uEnd;
pp1 = ppData2;
pp2 = ppData;
}
k = 0;
pp[k] = pp1[u1];
j = v1;
for (i = u1+1; i <= u2; i++ )
{
while ( j <= v2 )
{
if ( (*cfunc)(pp2[j], pp1[i]) < 0 )
{
++k;
pp[k] = pp2[j];
j++;
}
else
{
break;
}
}
++k;
pp[k] = pp1[i];
}
if ( j < v2 )
{
for ( i = j; i <= v2; i++)
{
++k;
pp[k] = pp2[i];
}
}
returnpp;
}
typedefstructSORTNODE_st {
void ** ppData;
UINT uStart;
UINT uEnd;
COMPAREFUNCfunc;
} SORTNODE;
DWORDWINAPIQuickSort_Thread(void *arg)
{
SORTNODE *pNode = (SORTNODE *)arg;
QuickSort(pNode->ppData, pNode->uStart, pNode->uEnd, pNode->func);
return 1;
}
#define THREAD_COUNT 4
INTMQuickSort(void **ppData, UINTuStart, UINTuEnd,
COMPAREFUNCCompareFunc)
{
void **pp1;
void **pp2;
void **pp3;
INT i;
SORTNODE Node[THREAD_COUNT];
HANDLE hThread[THREAD_COUNT];
INT nRet = CAPI_FAILED;
for ( i = 0; i < THREAD_COUNT; i++)
{
Node[i].ppData = ppData;
if ( i == 0 )
{
Node[i].uStart = uStart;
}
else
{
Node[i].uStart = uEnd * i /THREAD_COUNT + 1;
}
Node[i].uEnd = uEnd *(i+1) / THREAD_COUNT;
Node[i].func = CompareFunc;
hThread[i] = CreateThread(NULL, 0, QuickSort_Thread, &(Node[i]), 0, NULL);
}
for ( i = 0; i < THREAD_COUNT; i++ )
{
WaitForSingleObject(hThread[i], INFINITE);
}
pp1 = Merge(ppData, uStart, uEnd/4, ppData, uEnd/4+1, uEnd/2, CompareFunc);
pp2 = Merge(ppData, uEnd/2+1, uEnd*3/4, ppData, uEnd*3/4+1, uEnd, CompareFunc);
if ( pp1 != NULL && pp2 != NULL )
{
pp3 = Merge(pp1, 0, uEnd/2-uStart, pp2, 0, uEnd - uEnd/2 - 1, CompareFunc);
if ( pp3 != NULL )
{
UINTi;
for ( i = uStart; i <= uEnd; i++)
{
ppData[i] = pp3[i-uStart];
}
free(pp3);
nRet = CAPI_SUCCESS;
}
}
if( pp1 != NULL)
{
free( pp1 );
}
if ( pp2 != NULL )
{
free( pp2 );
}
returnnRet;
}
用下面程序来测试一下排1百万个随机整数的花费时间：
voidTest_MQuickSort (void)
{
UINTi;
UINTuCount = 1000000; //1000个
srand(time(NULL));
void **pp = (void **)malloc(uCount * sizeof(void *));
for ( i = 0; i < uCount; i++ )
{
pp[i] = (void *)(rand() % uCount);
}
clock_tt1 = clock();
INTnRet = MQuickSort(pp, 0, uCount-1, UIntCompare);
clock_tt2 = clock();
printf("MQuickSort 1000000 Time %ld\n", t2-t1);
free(pp);
}
在双核CPU上运行后，打印出花费的时间为 281 ms 比单任务版的快速排序函数快了188ms左右，效率提高了 188/281 = 67% 左右。
在单核CPU上运行上面的Test_MQuickSort函数，花费的时间约为532ms.
可见双核CPU中，多任务程序速度还是有很大提高的。
当然上面的多任务版的快速排序程序还有很大的改进余地，当对4个区间排好序后，后面的归并操作都是在一个任务里运行的，对整体效率会产生影响。估计将程序继续优化后，速度还能再快一些。
