以下是测试代码。
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
答案 0 :(得分：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，我运行了你的例子，但是首先保留了向量的正确大小以消除内存分配开销。有了这个，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：std::vector performance regression when enabling C++11
确实，启用-flto可以平衡我的结果：
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，失败的内联可能会在严格模板化的代码上产生极端结果。使用perf stat了解正在发生的事情。
答案 1 :(得分：36)
milianw没有解决-O0与-O2的问题，因此我想为此添加解释。
当未优化时，完全可以预期std::tuple将慢于std::pair，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，你有两倍的函数调用。
现在，当进行优化时，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。可以编写模板来提供没有或只有非常少的运行时开销的抽象，但这依赖于优化来内联所有简单的函数。