I mean: how can I measure time my CPU spent on function execution and wall clock time it takes to run my function? (Im interested in Linux/Windows and both x86 and x86_64). See what I want to do (Im using C++ here but I would prefer C solution):
int startcputime, endcputime, wcts, wcte;
startcputime = cputime();
function(args);
endcputime = cputime();
std::cout << "it took " << endcputime - startcputime << " s of CPU to execute this\n";
wcts = wallclocktime();
function(args);
wcte = wallclocktime();
std::cout << "it took " << wcte - wcts << " s of real time to execute this\n";
Another important question: is this type of time measuring architecture independent or not?
Here's a copy-paste solution that works on both Windows and Linux as well as C and C++.
As mentioned in the comments, there's a boost library that does this. But if you can't use boost, this should work:
// Windows
#ifdef _WIN32
#include <Windows.h>
double get_wall_time(){
LARGE_INTEGER time,freq;
if (!QueryPerformanceFrequency(&freq)){
// Handle error
return 0;
}
if (!QueryPerformanceCounter(&time)){
// Handle error
return 0;
}
return (double)time.QuadPart / freq.QuadPart;
}
double get_cpu_time(){
FILETIME a,b,c,d;
if (GetProcessTimes(GetCurrentProcess(),&a,&b,&c,&d) != 0){
// Returns total user time.
// Can be tweaked to include kernel times as well.
return
(double)(d.dwLowDateTime |
((unsigned long long)d.dwHighDateTime << 32)) * 0.0000001;
}else{
// Handle error
return 0;
}
}
// Posix/Linux
#else
#include <time.h>
#include <sys/time.h>
double get_wall_time(){
struct timeval time;
if (gettimeofday(&time,NULL)){
// Handle error
return 0;
}
return (double)time.tv_sec + (double)time.tv_usec * .000001;
}
double get_cpu_time(){
return (double)clock() / CLOCKS_PER_SEC;
}
#endif
There's a bunch of ways to implement these clocks. But here's what the above snippet uses:
For Windows:
GetProcessTimes()
For Linux:
gettimeofday()
clock()
And here's a small demonstration:
#include <math.h>
#include <iostream>
using namespace std;
int main(){
// Start Timers
double wall0 = get_wall_time();
double cpu0 = get_cpu_time();
// Perform some computation.
double sum = 0;
#pragma omp parallel for reduction(+ : sum)
for (long long i = 1; i < 10000000000; i++){
sum += log((double)i);
}
// Stop timers
double wall1 = get_wall_time();
double cpu1 = get_cpu_time();
cout << "Wall Time = " << wall1 - wall0 << endl;
cout << "CPU Time = " << cpu1 - cpu0 << endl;
// Prevent Code Elimination
cout << endl;
cout << "Sum = " << sum << endl;
}
Output (12 threads):
Wall Time = 15.7586
CPU Time = 178.719
Sum = 2.20259e+011