Ale*_*dar 42 performance c++11 stdtuple std-pair
这是测试代码.
元组测试:
using namespace std;
int main(){
vector<tuple<int,int>> v;
for (int var = 0; var < 100000000; ++var) {
v.push_back(make_tuple(var, var));
}
}
配对测试:
#include <vector>
using namespace std;
int main(){
vector<pair<int,int>> v;
for (int var = 0; var < 100000000; ++var) {
v.push_back(make_pair(var, var));
}
}
我通过Linux time命令进行了时间测量.结果是:
| | -O0 | -O2 |
|:------|:-------:|:--------:|
| Pair | 8.9 s | 1.60 s |
| Tuple | 19.8 s | 1.96 s |
我想知道,为什么O0中的这两个数据结构之间存在如此大的差异,因为它们应该非常相似.02中只有一点不同.
为什么O0的差异如此之大,为什么会有任何差异呢？
编辑:
v.resize()的代码
对:
#include <vector>
using namespace std;
int main(){
vector<pair<int,int>> v;
v.resize(100000000);
for (int var = 0; var < 100000000; ++var) {
v[var] = make_pair(var, var);
}
}
元组:
#include<tuple>
#include<vector>
using namespace std;
int main(){
vector<tuple<int,int>> v;
v.resize(100000000);
for (int var = 0; var < 100000000; ++var) {
v[var] = make_tuple(var, var);
}
}
结果:
| | -O0 | -O2 |
|:------|:-------:|:--------:|
| Pair | 5.01 s | 0.77 s |
| Tuple | 10.6 s | 0.87 s |
编辑:
我的系统
g++ (GCC) 4.8.3 20140911 (Red Hat 4.8.3-7)
GLIBCXX_3.4.19
mil*_*anw 64
您缺少一些重要信息:您使用什么编译器？你用什么来衡量微基准的性能？您使用什么标准库实现？
我的系统:
g++ (GCC) 4.9.1 20140903 (prerelease)
GLIBCXX_3.4.20
无论如何,我运行你的例子,但首先保留适当大小的向量,以摆脱内存分配开销.有了它,我有趣地观察到相反的东西 - 与你看到的相反:
g++ -std=c++11 -O2 pair.cpp -o pair
perf stat -r 10 -d ./pair
Performance counter stats for './pair' (10 runs):
1647.045151 task-clock:HG (msec) # 0.993 CPUs utilized ( +- 1.94% )
346 context-switches:HG # 0.210 K/sec ( +- 40.13% )
7 cpu-migrations:HG # 0.004 K/sec ( +- 22.01% )
182,978 page-faults:HG # 0.111 M/sec ( +- 0.04% )
3,394,685,602 cycles:HG # 2.061 GHz ( +- 2.24% ) [44.38%]
2,478,474,676 stalled-cycles-frontend:HG # 73.01% frontend cycles idle ( +- 1.24% ) [44.55%]
1,550,747,174 stalled-cycles-backend:HG # 45.68% backend cycles idle ( +- 1.60% ) [44.66%]
2,837,484,461 instructions:HG # 0.84 insns per cycle
# 0.87 stalled cycles per insn ( +- 4.86% ) [55.78%]
526,077,681 branches:HG # 319.407 M/sec ( +- 4.52% ) [55.82%]
829,623 branch-misses:HG # 0.16% of all branches ( +- 4.42% ) [55.74%]
594,396,822 L1-dcache-loads:HG # 360.887 M/sec ( +- 4.74% ) [55.59%]
20,842,113 L1-dcache-load-misses:HG # 3.51% of all L1-dcache hits ( +- 0.68% ) [55.46%]
5,474,166 LLC-loads:HG # 3.324 M/sec ( +- 1.81% ) [44.23%]
<not supported> LLC-load-misses:HG
1.658671368 seconds time elapsed ( +- 1.82% )
与:
g++ -std=c++11 -O2 tuple.cpp -o tuple
perf stat -r 10 -d ./tuple
Performance counter stats for './tuple' (10 runs):
996.090514 task-clock:HG (msec) # 0.996 CPUs utilized ( +- 2.41% )
102 context-switches:HG # 0.102 K/sec ( +- 64.61% )
4 cpu-migrations:HG # 0.004 K/sec ( +- 32.24% )
181,701 page-faults:HG # 0.182 M/sec ( +- 0.06% )
2,052,505,223 cycles:HG # 2.061 GHz ( +- 2.22% ) [44.45%]
1,212,930,513 stalled-cycles-frontend:HG # 59.10% frontend cycles idle ( +- 2.94% ) [44.56%]
621,104,447 stalled-cycles-backend:HG # 30.26% backend cycles idle ( +- 3.48% ) [44.69%]
2,700,410,991 instructions:HG # 1.32 insns per cycle
# 0.45 stalled cycles per insn ( +- 1.66% ) [55.94%]
486,476,408 branches:HG # 488.386 M/sec ( +- 1.70% ) [55.96%]
959,651 branch-misses:HG # 0.20% of all branches ( +- 4.78% ) [55.82%]
547,000,119 L1-dcache-loads:HG # 549.147 M/sec ( +- 2.19% ) [55.67%]
21,540,926 L1-dcache-load-misses:HG # 3.94% of all L1-dcache hits ( +- 2.73% ) [55.43%]
5,751,650 LLC-loads:HG # 5.774 M/sec ( +- 3.60% ) [44.21%]
<not supported> LLC-load-misses:HG
1.000126894 seconds time elapsed ( +- 2.47% )
正如您所看到的,在我的情况下,原因是在前端和后端都有更多的停滞周期.
现在它来自哪里？我打赌它归结为一些失败的内联,类似于这里解释的:启用C++ 11时的std :: vector performance regression
实际上,启用-flto均衡了我的结果:
Performance counter stats for './pair' (10 runs):
1021.922944 task-clock:HG (msec) # 0.997 CPUs utilized ( +- 1.15% )
63 context-switches:HG # 0.062 K/sec ( +- 77.23% )
5 cpu-migrations:HG # 0.005 K/sec ( +- 34.21% )
195,396 page-faults:HG # 0.191 M/sec ( +- 0.00% )
2,109,877,147 cycles:HG # 2.065 GHz ( +- 0.92% ) [44.33%]
1,098,031,078 stalled-cycles-frontend:HG # 52.04% frontend cycles idle ( +- 0.93% ) [44.46%]
701,553,535 stalled-cycles-backend:HG # 33.25% backend cycles idle ( +- 1.09% ) [44.68%]
3,288,420,630 instructions:HG # 1.56 insns per cycle
# 0.33 stalled cycles per insn ( +- 0.88% ) [55.89%]
672,941,736 branches:HG # 658.505 M/sec ( +- 0.80% ) [56.00%]
660,278 branch-misses:HG # 0.10% of all branches ( +- 2.05% ) [55.93%]
474,314,267 L1-dcache-loads:HG # 464.139 M/sec ( +- 1.32% ) [55.73%]
19,481,787 L1-dcache-load-misses:HG # 4.11% of all L1-dcache hits ( +- 0.80% ) [55.51%]
5,155,678 LLC-loads:HG # 5.045 M/sec ( +- 1.69% ) [44.21%]
<not supported> LLC-load-misses:HG
1.025083895 seconds time elapsed ( +- 1.03% )
和元组:
Performance counter stats for './tuple' (10 runs):
1018.980969 task-clock:HG (msec) # 0.999 CPUs utilized ( +- 0.47% )
8 context-switches:HG # 0.008 K/sec ( +- 29.74% )
3 cpu-migrations:HG # 0.003 K/sec ( +- 42.64% )
195,396 page-faults:HG # 0.192 M/sec ( +- 0.00% )
2,103,574,740 cycles:HG # 2.064 GHz ( +- 0.30% ) [44.28%]
1,088,827,212 stalled-cycles-frontend:HG # 51.76% frontend cycles idle ( +- 0.47% ) [44.56%]
697,438,071 stalled-cycles-backend:HG # 33.15% backend cycles idle ( +- 0.41% ) [44.76%]
3,305,631,646 instructions:HG # 1.57 insns per cycle
# 0.33 stalled cycles per insn ( +- 0.21% ) [55.94%]
675,175,757 branches:HG # 662.599 M/sec ( +- 0.16% ) [56.02%]
656,205 branch-misses:HG # 0.10% of all branches ( +- 0.98% ) [55.93%]
475,532,976 L1-dcache-loads:HG # 466.675 M/sec ( +- 0.13% ) [55.69%]
19,430,992 L1-dcache-load-misses:HG # 4.09% of all L1-dcache hits ( +- 0.20% ) [55.49%]
5,161,624 LLC-loads:HG # 5.065 M/sec ( +- 0.47% ) [44.14%]
<not supported> LLC-load-misses:HG
1.020225388 seconds time elapsed ( +- 0.48% )
所以请记住,-flto是你的朋友和失败的内联可以在严格模板化的代码上产生极端的结果.使用perf stat找出发生了什么.
Jan*_*dec 37
milianw没有涉及-O0对-O2,所以我想补充说明了点.
完全可以预期它std::tuple会比未优化std::pair时慢,因为它是更复杂的对象.一对只有两个成员,所以它的方法很容易定义.但是元组有任意数量的成员,迭代模板参数列表的唯一方法是使用递归.因此,元组的大多数函数处理一个成员然后递归以处理其余成员,因此对于2元组,您有两倍的函数调用.
现在,当它们被优化时,编译器将内联递归并且不应存在显着差异.哪些测试明确证实.这适用于一般的模板化程度很高的东西.可以编写模板以提供没有或很少运行时开销的抽象,但是依赖于优化来内联所有简单的函数.