我的C ++ 11程序正在执行序列化数据的联机处理,并且循环需要在数百万个内存位置上运行。必须提高计算效率,而我担心的是,通过在这样的循环内调用函数或类将创建不必要的操作,从而影响效率,例如,在不同变量范围之间传输该操作所需的几个指针值。
为了举例说明,让我们考虑以下虚拟示例,其中“某物”是重复的操作。请注意,“某物”中的代码使用循环范围内的变量。
do {
something(&span,&foo);
spam++
foo++
if ( spam == spam_spam ) {
something(&span,&foo);
other_things(&span,&foo);
something(&span,&foo);
}
else {
something(&span,&foo);
still_other_things(&span,&foo);
something(&span,&foo);
}
}
while (foo<bar);
有没有一种方法可以重复代码块,并避免使用不必要的操作来移动和复制变量?在此类循环上使用函数和类实际上是否意味着要进行其他操作,又如何避免这种操作?
更新资料
如建议的那样,我使用下面显示的代码进行了一些测试。我测试了几种方法来调用一个简单的增量1亿次。我在Hyper-V下的x86_64虚拟机上通过RHEL 7 Server 7.6使用GCC。
最初,使用“ g ++ -std = c ++ 17 -o test.o test.cpp”进行编译
简单循环计算(基准):211.046ms
内联函数:468.768ms
Lambda函数:253.466ms
定义宏:211.995ms
函数传递值:466.986ms
函数传递指针:344.646ms
无效功能:190.557ms
对象操作的对象方法:231.458ms
对象方法传递值:227.615ms
从这些结果中,我意识到编译器没有采用内联建议,即使按照g ++的建议将其夸大后也没有内联函数
后来,正如在同一篇文章中Mat的答案所建议的那样,我使用“ g ++ -std = c ++ 17 -O2 -o test.o test.cpp”打开了编译器优化,并获得了相同编号的以下结果迭代次数与未经优化的测试相比。
简单循环计算(基准):62.9254ms
内联函数:65.0564ms
Lambda函数:32.8637ms
定义宏:63.0299ms
函数传递值:64.2876ms
函数传递指针:63.3416ms
无效功能:32.1073ms
使用成员操作的对象方法:63.3847ms
对象方法传递值:62.5151ms
结论到此为止:
内联函数不是很好的选择,因为无法确定编译器将如何真正使用它,并且结果可能与使用标准函数一样糟糕。
“定义宏”和“ lambda函数”是内联的更好选择。每个都有其优点和功能,#define更灵活。
使用对象成员和方法可以很好地平衡解决任何情况下的问题,同时以易于维护和优化的形式维护代码。
调整编译器是值得的;
遵循用于测试的代码:
// Libraries
#include <iostream>
#include <cmath>
#include <chrono>
// Namespaces
using namespace std;
using namespace std::chrono;
// constants that control program behaviour
const long END_RESULT = 100000000;
const double AVERAGING_LENGTH = 40.0;
const int NUMBER_OF_ALGORITHM = 9;
const long INITIAL_VALUE = 0;
const long INCREMENT = 1;
// Global variables used for test with void function and to general control of the program;
long global_variable;
long global_increment;
// Function that returns the execution time for a simple loop
int64_t simple_loop_computation(long local_variable, long local_increment) {
// Starts the clock to measure the execution time for the baseline
high_resolution_clock::time_point timer_start = high_resolution_clock::now();
// Perform the computation for baseline
do {
local_variable += local_increment;
} while ( local_variable != END_RESULT);
// Stop the clock to measure performance of the silly version
high_resolution_clock::time_point timer_stop = high_resolution_clock::now();
return(duration_cast<microseconds>( timer_stop - timer_start ).count());
}
// Functions that computes the execution time when using inline code within the loop
inline long increment_variable() __attribute__((always_inline));
inline long increment_variable(long local_variable, long local_increment) {
return local_variable += local_increment;
}
int64_t inline_computation(long local_variable, long local_increment) {
// Starts the clock to measure the execution time for the baseline
high_resolution_clock::time_point timer_start = high_resolution_clock::now();
// Perform the computation for baseline
do {
local_variable = increment_variable(local_variable,local_increment);
} while ( local_variable != END_RESULT);
// Stop the clock to measure performance of the silly version
high_resolution_clock::time_point timer_stop = high_resolution_clock::now();
return duration_cast<microseconds>( timer_stop - timer_start ).count();
}
// Functions that computes the execution time when using lambda code within the loop
int64_t labda_computation(long local_variable, long local_increment) {
// Starts the clock to measure the execution time for the baseline
high_resolution_clock::time_point timer_start = high_resolution_clock::now();
// define lambda function
auto lambda_increment = [&] {
local_variable += local_increment;
};
// Perform the computation for baseline
do {
lambda_increment();
} while ( local_variable != END_RESULT);
// Stop the clock to measure performance of the silly version
high_resolution_clock::time_point timer_stop = high_resolution_clock::now();
return duration_cast<microseconds>( timer_stop - timer_start ).count();
}
// define lambda function
#define define_increment() local_variable += local_increment;
// Functions that computes the execution time when using lambda code within the loop
int64_t define_computation(long local_variable, long local_increment) {
// Starts the clock to measure the execution time for the baseline
high_resolution_clock::time_point timer_start = high_resolution_clock::now();
// Perform the computation for baseline
do {
define_increment();
} while ( local_variable != END_RESULT);
// Stop the clock to measure performance of the silly version
high_resolution_clock::time_point timer_stop = high_resolution_clock::now();
return duration_cast<microseconds>( timer_stop - timer_start ).count();
}
// Functions that compute the execution time when calling a function within the loop passing variable values
long increment_with_values_function(long local_variable, long local_increment) {
return local_variable += local_increment;
}
int64_t function_values_computation(long local_variable, long local_increment) {
// Starts the clock to measure the execution time for the baseline
high_resolution_clock::time_point timer_start = high_resolution_clock::now();
// Perform the computation for baseline
do {
local_variable = increment_with_values_function(local_variable,local_increment);
} while ( local_variable != END_RESULT);
// Stop the clock to measure performance of the silly version
high_resolution_clock::time_point timer_stop = high_resolution_clock::now();
return duration_cast<microseconds>( timer_stop - timer_start ).count();
}
// Functions that compute the execution time when calling a function within the loop passing variable pointers
long increment_with_pointers_function(long *local_variable, long *local_increment) {
return *local_variable += *local_increment;
}
int64_t function_pointers_computation(long local_variable, long local_increment) {
// Starts the clock to measure the execution time for the baseline
high_resolution_clock::time_point timer_start = high_resolution_clock::now();
// Perform the computation for baseline
do {
local_variable = increment_with_pointers_function(&local_variable,&local_increment);
} while ( local_variable != END_RESULT);
// Stop the clock to measure performance of the silly version
high_resolution_clock::time_point timer_stop = high_resolution_clock::now();
return duration_cast<microseconds>( timer_stop - timer_start ).count();
}
// Functions that compute the execution time when calling a function within the loop without passing variables
void increment_with_void_function(void) {
global_variable += global_increment;
}
int64_t function_void_computation(long local_variable, long local_increment) {
// Starts the clock to measure the execution time for the baseline
high_resolution_clock::time_point timer_start = high_resolution_clock::now();
// set global variables
global_variable = local_variable;
global_increment = local_increment;
// Perform the computation for baseline
do {
increment_with_void_function();
} while ( global_variable != END_RESULT);
// Stop the clock to measure performance of the silly version
high_resolution_clock::time_point timer_stop = high_resolution_clock::now();
return duration_cast<microseconds>( timer_stop - timer_start ).count();
}
// Object and Function that compute the duration when using a method of the object where data is stored without passing variables
struct object {
long object_variable = 0;
long object_increment = 1;
object(long local_variable, long local_increment) {
object_variable = local_variable;
object_increment = local_increment;
}
void increment_object(void){
object_variable+=object_increment;
}
void increment_object_with_value(long local_increment){
object_variable+=local_increment;
}
};
int64_t object_members_computation(long local_variable, long local_increment) {
// Starts the clock to measure the execution time for the baseline
high_resolution_clock::time_point timer_start = high_resolution_clock::now();
// Create object
object object_instance = {local_variable,local_increment};
// Perform the computation for baseline
do {
object_instance.increment_object();
} while ( object_instance.object_variable != END_RESULT);
// Get the results out of the object
local_variable = object_instance.object_variable;
// Stop the clock to measure performance of the silly version
high_resolution_clock::time_point timer_stop = high_resolution_clock::now();
return duration_cast<microseconds>( timer_stop - timer_start ).count();
}
// Function that compute the duration when using a method of the object where data is stored passing variables
int64_t object_values_computation(long local_variable, long local_increment) {
// Starts the clock to measure the execution time for the baseline
high_resolution_clock::time_point timer_start = high_resolution_clock::now();
// Create object
object object_instance = {local_variable,local_increment};
// Perform the computation for baseline
do {
object_instance.increment_object_with_value(local_increment);
} while ( object_instance.object_variable != END_RESULT);
// Get the results out of the object
local_variable = object_instance.object_variable;
// Stop the clock to measure performance of the silly version
high_resolution_clock::time_point timer_stop = high_resolution_clock::now();
return duration_cast<microseconds>( timer_stop - timer_start ).count();
}
int main() {
// Create array to store execution time results for all tests
pair<string,int64_t> duration_sum[NUMBER_OF_ALGORITHM]={
make_pair("Simple loop computation (baseline): ",0.0),
make_pair("Inline Function: ",0.0),
make_pair("Lambda Function: ",0.0),
make_pair("Define Macro: ",0.0)
make_pair("Function passing values: ",0.0),
make_pair("Function passing pointers: ",0.0),
make_pair("Function with void: ",0.0),
make_pair("Object method operating with members: ",0.0),
make_pair("Object method passing values: ",0.0),
};
// loop to compute average of several execution times
for ( int i = 0; i < AVERAGING_LENGTH; i++) {
// Compute the execution time for a simple loop as the baseline
duration_sum[0].second = duration_sum[0].second + simple_loop_computation(INITIAL_VALUE, INCREMENT);
// Compute the execution time when using inline code within the loop (expected same as baseline)
duration_sum[1].second = duration_sum[1].second + inline_computation(INITIAL_VALUE, INCREMENT);
// Compute the execution time when using lambda code within the loop (expected same as baseline)
duration_sum[2].second = duration_sum[2].second + labda_computation(INITIAL_VALUE, INCREMENT);
// Compute the duration when using a define macro
duration_sum[3].second = duration_sum[3].second + define_computation(INITIAL_VALUE, INCREMENT);
// Compute the execution time when calling a function within the loop passing variables values
duration_sum[4].second = duration_sum[4].second + function_values_computation(INITIAL_VALUE, INCREMENT);
// Compute the execution time when calling a function within the loop passing variables pointers
duration_sum[5].second = duration_sum[5].second + function_pointers_computation(INITIAL_VALUE, INCREMENT);
// Compute the execution time when calling a function within the loop without passing variables
duration_sum[6].second = duration_sum[6].second + function_void_computation(INITIAL_VALUE, INCREMENT);
// Compute the duration when using a method of the object where data is stored without passing variables
duration_sum[7].second = duration_sum[7].second + object_members_computation(INITIAL_VALUE, INCREMENT);
// Compute the duration when using a method of the object where data is stored passing variables
duration_sum[8].second = duration_sum[8].second + object_values_computation(INITIAL_VALUE, INCREMENT);
}
double average_baseline_duration = 0.0;
// Print out results
for ( int i = 0; i < NUMBER_OF_ALGORITHM; i++) {
// compute averave from sum
average_baseline_duration = ((double)duration_sum[i].second/AVERAGING_LENGTH)/1000.0;
// Print the result
cout << duration_sum[i].first << average_baseline_duration << "ms \n";
}
return 0;
}
如果代码足够短,则可以内联声明它,编译器会将其内联。如果不是,那么,可能就无济于事了。
但是,老实说,这是最不有效的优化形式。关注高效算法和缓存高效数据结构。
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