mez*_*hic 5 c++ cpu optimization performance compiler-optimization
我有两种代码迭代大小为500的矢量设计.其中一个设计包含64位双精度数组,第二个设计使用包含32位整数的数组.我期待32位设计更快,因为更多有用的数据可以打包在缓存中.
编译器MSVC,CPU Ivy Bridge,编译64位模式.
这是代码1,使用32位整数(在2600个 CPU周期中运行):
#include <vector>
#include <iostream>
int main(){
std::vector<unsigned int> x1;
std::vector<unsigned int> x2;
std::vector<unsigned int> x3;
x1.resize(500);
x2.resize(500);
x3.resize(500);
for(int i =0; i<500; i++){
x1[i] = i;
x2[i] = 2*i;
x3[i] = 4*i;
}
int counter = 0;
while(counter < 1000){
unsigned long long start = 0;
unsigned long long end = 0;
double m = 0;
double n = 0;
start = __rdtsc();
for(int i=0; i < 500; i++){
unsigned int a = x1[i];
unsigned int b = x2[i];
unsigned int g = x3[i];
m = m + (a * g);
n = n + (b * g);
}
end = __rdtscp();
std::cout << (end-start) << "\t\t"<<m << n << std::endl;
counter++;
}
}
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产生这个asm(-Os):
start = __rdtscp(&p);
rdtscp
lea r8,[rbp+6Fh]
mov dword ptr [r8],ecx
shl rdx,20h
or rax,rdx
mov r10,rax
unsigned int p;
unsigned int q;
unsigned long long start = 0;
unsigned long long end = 0;
double m = 0;
mov r8,rbx
mov r9d,1F4h
unsigned int a = x1[i];
unsigned int b = x2[i];
unsigned int g = x3[i];
mov edx,dword ptr [r8+r15]
m = m + (a * g);
mov ecx,edx
imul ecx,dword ptr [r8+r14]
xorps xmm0,xmm0
cvtsi2sd xmm0,rcx
addsd xmm7,xmm0
n = n + (b * g);
imul edx,dword ptr [r8]
mov eax,edx
xorps xmm0,xmm0
cvtsi2sd xmm0,rax
addsd xmm8,xmm0
for(int i=0; i < 500; i++){
add r8,4
dec r9
jne main+0E5h (013F681261h)
}
end = __rdtscp(&q);
rdtscp
}
end = __rdtscp(&q);
lea r8,[rbp+6Fh]
mov dword ptr [r8],ecx
shl rdx,20h
or rdx,rax
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这是代码2,使用64位双精度(代码在2000个 CPU周期中运行):
#include <vector>
#include <iostream>
int main(){
std::vector<double> x1;
std::vector<double> x2;
std::vector<unsigned long long> x3;
x1.resize(500);
x2.resize(500);
x3.resize(500);
for(int i =0; i<500; i++){
x1[i] = i;
x2[i] = 2*i;
x3[i] = 4*i;
}
int counter = 0;
while(counter < 1000){
unsigned int p;
unsigned int q;
unsigned long long start = 0;
unsigned long long end = 0;
double m = 0;
double n = 0;
start = __rdtscp(&p);
for(int i=0; i < 500; i++){
double a = x1[i];
double b = x2[i];
unsigned long long g = x3[i];
m = m + (a * g);
n = n + (b * g);
}
end = __rdtscp(&q);
std::cout << (end-start) << "\t\t"<<m << n << std::endl;
counter++;
}
}
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这里是asm(-Os)产生的:
start = __rdtscp(&p);
rdtscp
lea r8,[rbp+6Fh]
mov dword ptr [r8],ecx
shl rdx,20h
or rax,rdx
mov r9,rax
unsigned int p;
unsigned int q;
unsigned long long start = 0;
unsigned long long end = 0;
double m = 0;
mov rdx,rbx
mov r8d,1F4h
double a = x1[i];
double b = x2[i];
unsigned long long g = x3[i];
mov rcx,qword ptr [rdx+r15]
xorps xmm1,xmm1
m = m + (a * g);
cvtsi2sd xmm1,rcx
test rcx,rcx
jns main+120h (013F32129Ch)
addsd xmm1,xmm9
movaps xmm0,xmm1
mulsd xmm0,mmword ptr [rdx+r14]
addsd xmm6,xmm0
n = n + (b * g);
mulsd xmm1,mmword ptr [rdx]
addsd xmm7,xmm1
for(int i=0; i < 500; i++){
add rdx,8
dec r8
jne main+10Ah (013F321286h)
}
end = __rdtscp(&q);
rdtscp
}
end = __rdtscp(&q);
lea r8,[rbp+6Fh]
mov dword ptr [r8],ecx
shl rdx,20h
or rdx,rax
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区别在于第一个代码中的整数转换为双精度(向量包含unsigned int,产品是整数运算,但积累使用double,在汇编程序中,这会将cvtsi2sd指令添加到代码中).
在第二个代码中,您在任何地方都使用双打,因此您没有转换,代码运行得更快.
这种差异将是多少更明显具有定点和浮点处理单元之间的严格区分在CPU上(在POWER平台是一个这样的例子).在这方面,X86平台非常宽容.