26
edits
Changes
→The Monte Carlo Simulation (PI Calculation)
It uses random sampling to define constraints on the value and then makes a sort of "best guess."
Source Code:
<pre>
#include<iostream>
#include<fstream>
#include<math.h>
#include<stdlib.h>
#include<time.h>
using namespace std;
void calculatePI(int n, float* h_a) {
float x, y;
int hit;
srand(time(NULL));
for (int j = 0; j < n; j++) {
hit = 0;
x = 0;
y = 0;
for (int i = 0; i < n; i++) {
x = float(rand()) / float(RAND_MAX);
y = float(rand()) / float(RAND_MAX);
if (y <= sqrt(1 - (x * x))) {
hit += 1;
}
}
h_a[j] = 4 * float(hit) / float(n);
}
}
int main(int argc, char* argv[]) {
if (argc != 2) {
std::cerr << argv[0] << ": invalid number of arguments\n";
std::cerr << "Usage: " << argv[0] << " size_of_matrices\n";
return 1;
}
int n = std::atoi(argv[1]); // scale
float* cpu_a;
cpu_a = new float[n];
calculatePI(n, cpu_a);
ofstream h_file;
h_file.open("h_result.txt");
float cpuSum = 0.0f;
for (int i = 0; i < n; i++) {
cpuSum += cpu_a[i];
h_file << "Host: " << cpu_a[i] << endl;
}
cpuSum = cpuSum / (float)n;
cout << "CPU Result: " << cpuSum << endl;
h_file.close();
}
</pre>
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