对 hash 进行了简单的性能测试,似乎 C++ 版本比 perl 版本和 golang 版本都慢。
- perl 版本花费了大约 200 毫秒,
- C++ 版本耗时 280 毫秒。
- golang 版本耗时 56 毫秒。
在配备 Core(TM) i7-2670QM CPU @ 2.20GHz、Ubuntu 14.04.3LTS 的 PC 上,
有什么想法吗?
perl 版本
use Time::HiRes qw( usleep ualarm gettimeofday tv_interval nanosleep
clock_gettime clock_getres clock_nanosleep clock
stat );
sub getTS {
my ($seconds, $microseconds) = gettimeofday;
return $seconds + (0.0+ $microseconds)/1000000.0;
}
my %mymap;
$mymap{"U.S."} = "Washington";
$mymap{"U.K."} = "London";
$mymap{"France"} = "Paris";
$mymap{"Russia"} = "Moscow";
$mymap{"China"} = "Beijing";
$mymap{"Germany"} = "Berlin";
$mymap{"Japan"} = "Tokyo";
$mymap{"China"} = "Beijing";
$mymap{"Italy"} = "Rome";
$mymap{"Spain"} = "Madrad";
$x = "";
$start = getTS();
for ($i=0; $i<1000000; $i++) {
$x = $mymap{"China"};
}
printf "took %f sec\n", getTS() - $start;
C++ 版本
#include <iostream>
#include <string>
#include <unordered_map>
#include <sys/time.h>
double getTS() {
struct timeval tv;
gettimeofday(&tv, NULL);
return tv.tv_sec + tv.tv_usec/1000000.0;
}
using namespace std;
int main () {
std::unordered_map<std::string,std::string> mymap;
// populating container:
mymap["U.S."] = "Washington";
mymap["U.K."] = "London";
mymap["France"] = "Paris";
mymap["Russia"] = "Moscow";
mymap["China"] = "Beijing";
mymap["Germany"] = "Berlin";
mymap["Japan"] = "Tokyo";
mymap["China"] = "Beijing";
mymap["Italy"] = "Rome";
mymap["Spain"] = "Madrad";
double start = getTS();
string x;
for (int i=0; i<1000000; i++) {
mymap["China"];
}
printf("took %f sec\n", getTS() - start);
return 0;
}
Go 语言版本
package main
import "fmt"
import "time"
func main() {
var x string
mymap := make(map[string]string)
mymap["U.S."] = "Washington";
mymap["U.K."] = "London";
mymap["France"] = "Paris";
mymap["Russia"] = "Moscow";
mymap["China"] = "Beijing";
mymap["Germany"] = "Berlin";
mymap["Japan"] = "Tokyo";
mymap["China"] = "Beijing";
mymap["Italy"] = "Rome";
mymap["Spain"] = "Madrad";
t0 := time.Now()
sum := 1
for sum < 1000000 {
x = mymap["China"]
sum += 1
}
t1 := time.Now()
fmt.Printf("The call took %v to run.\n", t1.Sub(t0))
fmt.Println(x)
}
更新 1
为了改进 C++ 版本,更改了 x = mymap["China"];
至 mymap["China"];
,但性能差别很小。
更新 2
我在没有任何优化的情况下编译时得到了原始结果:g++ -std=c++11 unorderedMap.cc
.使用“-O2”优化,只花费一半左右的时间(150ms)
更新 3
删除可能的char*
至 string
构造函数调用,我创建了一个字符串常量。时间减少到大约 220 毫秒(编译时没有优化)。感谢@neil-kirk 的建议,经过优化(-O2 标志),时间约为 80 毫秒。
double start = getTS();
string x = "China";
for (int i=0; i<1000000; i++) {
mymap[x];
}
更新 4
感谢 @steffen-ullrich 指出 perl 版本存在语法错误。我改变了它。性能数约为150ms。
更新 5
看来执行指令的数量很重要。使用命令 valgrind --tool=cachegrind <cmd>
Go 版本
$ valgrind --tool=cachegrind ./te1
==2103== Cachegrind, a cache and branch-prediction profiler
==2103== Copyright (C) 2002-2013, and GNU GPL'd, by Nicholas Nethercote et al.
==2103== Using Valgrind-3.10.0.SVN and LibVEX; rerun with -h for copyright info
==2103== Command: ./te1
==2103==
--2103-- warning: L3 cache found, using its data for the LL simulation.
The call took 1.647099s to run.
Beijing
==2103==
==2103== I refs: 255,763,381
==2103== I1 misses: 3,709
==2103== LLi misses: 2,743
==2103== I1 miss rate: 0.00%
==2103== LLi miss rate: 0.00%
==2103==
==2103== D refs: 109,437,132 (77,838,331 rd + 31,598,801 wr)
==2103== D1 misses: 352,474 ( 254,714 rd + 97,760 wr)
==2103== LLd misses: 149,260 ( 96,250 rd + 53,010 wr)
==2103== D1 miss rate: 0.3% ( 0.3% + 0.3% )
==2103== LLd miss rate: 0.1% ( 0.1% + 0.1% )
==2103==
==2103== LL refs: 356,183 ( 258,423 rd + 97,760 wr)
==2103== LL misses: 152,003 ( 98,993 rd + 53,010 wr)
==2103== LL miss rate: 0.0% ( 0.0% + 0.1% )
针对C++优化版(无优化标志)
$ valgrind --tool=cachegrind ./a.out
==2180== Cachegrind, a cache and branch-prediction profiler
==2180== Copyright (C) 2002-2013, and GNU GPL'd, by Nicholas Nethercote et al.
==2180== Using Valgrind-3.10.0.SVN and LibVEX; rerun with -h for copyright info
==2180== Command: ./a.out
==2180==
--2180-- warning: L3 cache found, using its data for the LL simulation.
took 64.657681 sec
==2180==
==2180== I refs: 5,281,474,482
==2180== I1 misses: 1,710
==2180== LLi misses: 1,651
==2180== I1 miss rate: 0.00%
==2180== LLi miss rate: 0.00%
==2180==
==2180== D refs: 3,170,495,683 (1,840,363,429 rd + 1,330,132,254 wr)
==2180== D1 misses: 12,055 ( 10,374 rd + 1,681 wr)
==2180== LLd misses: 7,383 ( 6,132 rd + 1,251 wr)
==2180== D1 miss rate: 0.0% ( 0.0% + 0.0% )
==2180== LLd miss rate: 0.0% ( 0.0% + 0.0% )
==2180==
==2180== LL refs: 13,765 ( 12,084 rd + 1,681 wr)
==2180== LL misses: 9,034 ( 7,783 rd + 1,251 wr)
==2180== LL miss rate: 0.0% ( 0.0% + 0.0% )
C++优化版
$ valgrind --tool=cachegrind ./a.out
==2157== Cachegrind, a cache and branch-prediction profiler
==2157== Copyright (C) 2002-2013, and GNU GPL'd, by Nicholas Nethercote et al.
==2157== Using Valgrind-3.10.0.SVN and LibVEX; rerun with -h for copyright info
==2157== Command: ./a.out
==2157==
--2157-- warning: L3 cache found, using its data for the LL simulation.
took 9.419447 sec
==2157==
==2157== I refs: 1,451,459,660
==2157== I1 misses: 1,599
==2157== LLi misses: 1,549
==2157== I1 miss rate: 0.00%
==2157== LLi miss rate: 0.00%
==2157==
==2157== D refs: 430,486,197 (340,358,108 rd + 90,128,089 wr)
==2157== D1 misses: 12,008 ( 10,337 rd + 1,671 wr)
==2157== LLd misses: 7,372 ( 6,120 rd + 1,252 wr)
==2157== D1 miss rate: 0.0% ( 0.0% + 0.0% )
==2157== LLd miss rate: 0.0% ( 0.0% + 0.0% )
==2157==
==2157== LL refs: 13,607 ( 11,936 rd + 1,671 wr)
==2157== LL misses: 8,921 ( 7,669 rd + 1,252 wr)
==2157== LL miss rate: 0.0% ( 0.0% + 0.0% )
最佳答案
来自您的 Perl 代码(在您尝试修复它之前):
@mymap = (); $mymap["U.S."] = "Washington"; $mymap["U.K."] = "London";
这不是 map 而是数组。 HashMap 的语法是:
%mymap;
$mymap{"U.S."} = ....
因此,您有效地做的是创建一个数组而不是 HashMap ,并始终访问元素 0。
请在 Perl 中始终使用 use strict;
和 use warnings;
,即使是简单的带有警告的语法检查也会向您显示问题所在:
perl -cw x.pl
Argument "U.S." isn't numeric in array element at x.pl line 9.
Argument "U.K." isn't numeric in array element at x.pl line 10.
除此之外,基准测试的主要部分实际上没有做任何有用的事情(分配一个变量并且从不使用它)并且一些编译器可以检测到它并简单地优化它。
如果您检查 Perl 程序生成的代码,您会看到:
$ perl -MO=Deparse x.pl
@mymap = ();
$mymap[0] = 'Washington';
$mymap[0] = 'London';
...
for ($i = 0; $i < 1000000; ++$i) {
$x = $mymap[0];
}
那是它在编译时检测到问题并用对数组索引 0 的访问替换它。
因此,无论何时进行基准测试,您都需要:
- 检查您的程序是否确实按照预期进行。
- 检查编译器是否没有优化某些东西,也没有在编译时执行其他语言在运行时执行的东西。任何类型的没有结果或可预测结果的语句都容易进行此类优化。
- 确认您确实衡量了您打算衡量的内容,而不是其他内容。即使对程序进行微小的更改也会影响运行时间,因为需要内存分配,而这些更改可能与您打算测量的内容无关。在您的基准测试中,您一次又一次地测量对同一散列元素的访问,而不访问其间的其他元素。这是一项可以很好地使用处理器缓存的事件,但可能无法反射(reflect)真实世界的使用情况。
而且,使用简单的计时器并不是一个现实的基准。系统上还有其他进程,有调度程序,有缓存垃圾……对于今天的 CPU,它在很大程度上取决于系统上的负载,因为 CPU 可能会以比其他基准测试更低的功耗模式运行一个基准测试,即使用不同的 CPU 时钟。例如,同一“基准测试”的多次运行在我的系统上的测量时间在 100 毫秒到 150 毫秒之间变化。
基准是谎言,像你这样的微观基准更是如此。
关于c++ - 哈希表的性能,为什么C++最慢?,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/33950565/