优化的合并排序比快速排序更快
[ http://jsperf.com/optimized-mergesort-versus- quicksort] [1]
为什么这个半缓冲区合并排序的工作速度与quicksort一样快?
[http://jsperf.com/optimized-mergesort-versus-quicksort][1]
Why does this half buffer merge sort work as fast as quicksort?
QuickSort是:
QuickSort is:
- 就地虽然会占用
log(n)递归(堆栈空间) - 缓存友好
- In-Place although it takes up
log(n)recursions (stack space) - Cache-Friendly
此半缓冲区合并排序:
- 使用
n/2缓冲区进行合并. - 使用
log(n)递归. - 进行较少的比较.
- Uses an
n/2Buffer to do merges. - Uses
log(n)recursions. - Makes fewer comparisons.
我的问题是，在这种情况下，为什么半缓冲区合并排序与QuickSort的速度匹配? 另外，我对quickSort所做的任何操作使它变慢了吗?
My question is, why is the half buffer merge sort matching the speed of QuickSort in this scenario? Plus, is there anything I'm doing wrong to the quickSort that makes it slower?
function partition(a, i, j) {
var p = i + Math.floor((j - i) / 2);
var left = i + 1;
var right = j;
swap(a, i, p);
var pivot = a[i];
while (left <= right) {
while (builtinLessThan(a[left], pivot)) {
++left;
}
while (builtinLessThan(pivot, a[right])) {
--right;
}
if (left <= right) {
swap(a, left, right);
++left;
--right;
}
}
swap(a, i, right);
return right;
};
function quickSort(a, i, j) {
var p = partition(a, i, j);
if ((p) - i > j - p) {
if (i < p - 1) {
quickSort(a, i, p - 1);
}
if (p + 1 < j) {
quickSort(a, p + 1, j);
}
} else {
if (p + 1 < j) {
quickSort(a, p + 1, j);
} if (i < p - 1) {
quickSort(a, i, p - 1);
}
}
};
合并排序的比较次数较少，但比快速排序的动作更多.必须调用函数进行比较会增加比较的开销，这会使快速排序变慢.快速排序示例中的所有if语句也使它变慢.如果比较和交换是内联完成的，那么如果对伪随机整数数组进行排序，则快速排序应该会更快一些.
Merge sort does fewer compares, but more moves than quick sort. Having to call a function to do the compares increases the overhead for compares, which makes quick sort slower. All those if statements in the example quick sort is also slowing it down. If the compare and swap are done inline, then quick sort should be a bit faster if sorting an array of pseudo random integers.
如果在具有16个寄存器的处理器(例如PC的64位模式)上运行，则使用一堆最终在寄存器中的指针进行的4方式合并排序的速度与快速排序的速度差不多. 2路合并排序平均值对每个移动的元素进行比较1，而4路合并排序平均值3对每个移动的元素进行比较，但只需要通过次数的1/2，因此基本操作的次数是相同的，但是比较起来对缓存的友好性更高，这使得4路合并排序的速度提高了约15％，与快速排序的速度差不多.
If running on a processor with 16 registers, such a PC in 64 bit mode, then 4 way merge sort using a bunch of pointers that end up in registers is about as fast as quick sort. A 2 way merge sort averages 1 compare for each element moved, while a 4 way merge sort averages 3 compares for each element moved, but only takes 1/2 the number of passes, so the number of basic operations is the same, but the compares are a bit more cache friendly, making the 4 way merge sort about 15% faster, about the same as quick sort.
我对Java脚本不熟悉，因此我将示例转换为C ++.
I'm not familiar with java script, so I'm converting the examples to C++.
使用Java脚本merge sort的转换版本，大约需要2.4秒才能对1600万个伪随机32位整数进行排序.下面显示的示例快速排序过程大约需要1.4秒，下面显示的示例自下而上的合并过程大约需要1.6秒.如前所述，在带有16个寄存器的处理器上使用一堆指针(或索引)进行4路合并也将花费大约1.4秒.
Using a converted version of the java script merge sort, it takes about 2.4 seconds to sort 16 million pseudo random 32 bit integers. The example quick sort shown below takes about 1.4 seconds, and the example bottom up merge shown below sort about 1.6 seconds. As mentioned, a 4 way merge using a bunch of pointers (or indices) on a processor with 16 registers would also take about 1.4 seconds.
C ++快速排序示例:
C++ quick sort example:
void QuickSort(int a[], int lo, int hi) {
int i = lo, j = hi;
int pivot = a[(lo + hi) / 2];
int t;
while (i <= j) { // partition
while (a[i] < pivot)
i++;
while (a[j] > pivot)
j--;
if (i <= j) {
t = a[i]
a[i] = a[j];
a[j] = t;
i++;
j--;
}
}
if (lo < j) // recurse
QuickSort(a, lo, j);
if (i < hi)
QuickSort(a, i, hi);
}
C ++自底向上合并排序示例:
C++ bottom up merge sort example:
void BottomUpMergeSort(int a[], int b[], size_t n)
{
size_t s = 1; // run size
if(GetPassCount(n) & 1){ // if odd number of passes
for(s = 1; s < n; s += 2) // swap in place for 1st pass
if(a[s] < a[s-1])
std::swap(a[s], a[s-1]);
s = 2;
}
while(s < n){ // while not done
size_t ee = 0; // reset end index
while(ee < n){ // merge pairs of runs
size_t ll = ee; // ll = start of left run
size_t rr = ll+s; // rr = start of right run
if(rr >= n){ // if only left run
rr = n;
BottomUpCopy(a, b, ll, rr); // copy left run
break; // end of pass
}
ee = rr+s; // ee = end of right run
if(ee > n)
ee = n;
BottomUpMerge(a, b, ll, rr, ee);
}
std::swap(a, b); // swap a and b
s <<= 1; // double the run size
}
}
void BottomUpMerge(int a[], int b[], size_t ll, size_t rr, size_t ee)
{
size_t o = ll; // b[] index
size_t l = ll; // a[] left index
size_t r = rr; // a[] right index
while(1){ // merge data
if(a[l] <= a[r]){ // if a[l] <= a[r]
b[o++] = a[l++]; // copy a[l]
if(l < rr) // if not end of left run
continue; // continue (back to while)
while(r < ee) // else copy rest of right run
b[o++] = a[r++];
break; // and return
} else { // else a[l] > a[r]
b[o++] = a[r++]; // copy a[r]
if(r < ee) // if not end of right run
continue; // continue (back to while)
while(l < rr) // else copy rest of left run
b[o++] = a[l++];
break; // and return
}
}
}
void BottomUpCopy(int a[], int b[], size_t ll, size_t rr)
{
while(ll < rr){ // copy left run
b[ll] = a[ll];
ll++;
}
}
size_t GetPassCount(size_t n) // return # passes
{
size_t i = 0;
for(size_t s = 1; s < n; s <<= 1)
i += 1;
return(i);
}
使用指针的4种方式进行合并排序的C ++示例(goto用于节省代码空间，它是旧代码).它开始进行4种方式的合并，然后在运行结束时切换到3种方式的合并，然后进行2种方式的合并，然后再复制剩余的剩余部分.这类似于用于外部排序的算法，但是外部排序逻辑更为通用，通常可以处理多达16种方式的合并.
C++ example of 4 way merge sort using pointers (goto's used to save code space, it's old code). It starts off doing 4 way merge, then when the end of a run is reached, it switches to 3 way merge, then 2 way merge, then a copy of what's left of the remaining run. This is similar to algorithms used for external sorts, but external sort logic is more generalized and often handles up to 16 way merges.
int * BottomUpMergeSort(int a[], int b[], size_t n)
{
int *p0r; // ptr to run 0
int *p0e; // ptr to end run 0
int *p1r; // ptr to run 1
int *p1e; // ptr to end run 1
int *p2r; // ptr to run 2
int *p2e; // ptr to end run 2
int *p3r; // ptr to run 3
int *p3e; // ptr to end run 3
int *pax; // ptr to set of runs in a
int *pbx; // ptr for merged output to b
size_t rsz = 1; // run size
if(n < 2)
return a;
if(n == 2){
if(a[0] > a[1])std::swap(a[0],a[1]);
return a;
}
if(n == 3){
if(a[0] > a[2])std::swap(a[0],a[2]);
if(a[0] > a[1])std::swap(a[0],a[1]);
if(a[1] > a[2])std::swap(a[1],a[2]);
return a;
}
while(rsz < n){
pbx = &b[0];
pax = &a[0];
while(pax < &a[n]){
p0e = rsz + (p0r = pax);
if(p0e >= &a[n]){
p0e = &a[n];
goto cpy10;}
p1e = rsz + (p1r = p0e);
if(p1e >= &a[n]){
p1e = &a[n];
goto mrg201;}
p2e = rsz + (p2r = p1e);
if(p2e >= &a[n]){
p2e = &a[n];
goto mrg3012;}
p3e = rsz + (p3r = p2e);
if(p3e >= &a[n])
p3e = &a[n];
// 4 way merge
while(1){
if(*p0r <= *p1r){
if(*p2r <= *p3r){
if(*p0r <= *p2r){
mrg40: *pbx++ = *p0r++; // run 0 smallest
if(p0r < p0e) // if not end run continue
continue;
goto mrg3123; // merge 1,2,3
} else {
mrg42: *pbx++ = *p2r++; // run 2 smallest
if(p2r < p2e) // if not end run continue
continue;
goto mrg3013; // merge 0,1,3
}
} else {
if(*p0r <= *p3r){
goto mrg40; // run 0 smallext
} else {
mrg43: *pbx++ = *p3r++; // run 3 smallest
if(p3r < p3e) // if not end run continue
continue;
goto mrg3012; // merge 0,1,2
}
}
} else {
if(*p2r <= *p3r){
if(*p1r <= *p2r){
mrg41: *pbx++ = *p1r++; // run 1 smallest
if(p1r < p1e) // if not end run continue
continue;
goto mrg3023; // merge 0,2,3
} else {
goto mrg42; // run 2 smallest
}
} else {
if(*p1r <= *p3r){
goto mrg41; // run 1 smallest
} else {
goto mrg43; // run 3 smallest
}
}
}
}
// 3 way merge
mrg3123: p0r = p1r;
p0e = p1e;
mrg3023: p1r = p2r;
p1e = p2e;
mrg3013: p2r = p3r;
p2e = p3e;
mrg3012: while(1){
if(*p0r <= *p1r){
if(*p0r <= *p2r){
*pbx++ = *p0r++; // run 0 smallest
if(p0r < p0e) // if not end run continue
continue;
goto mrg212; // merge 1,2
} else {
mrg32: *pbx++ = *p2r++; // run 2 smallest
if(p2r < p2e) // if not end run continue
continue;
goto mrg201; // merge 0,1
}
} else {
if(*p1r <= *p2r){
*pbx++ = *p1r++; // run 1 smallest
if(p1r < p1e) // if not end run continue
continue;
goto mrg202; // merge 0,2
} else {
goto mrg32; // run 2 smallest
}
}
}
// 2 way merge
mrg212: p0r = p1r;
p0e = p1e;
mrg202: p1r = p2r;
p1e = p2e;
mrg201: while(1){
if(*p0r <= *p1r){
*pbx++ = *p0r++; // run 0 smallest
if(p0r < p0e) // if not end run continue
continue;
goto cpy11;
} else {
*pbx++ = *p1r++; // run 1 smallest
if(p1r < p1e) // if not end run continue
continue;
goto cpy10;
}
}
// 1 way copy
cpy11: p0r = p1r;
p0e = p1e;
cpy10: while (1) {
*pbx++ = *p0r++; // copy element
if (p0r < p0e) // if not end of run continue
continue;
break;
}
pax += rsz << 2; // setup for next set of runs
}
std::swap(a, b); // swap ptrs
rsz <<= 2; // quadruple run size
}
return a; // return sorted array
}