I was asked to check the calculation time depending on the number of threads working on the problem. So I wrote a program that calculates the integral using the Monte Carlo method. I am dividing the range by the number of threads. After that, I define the flows that calculate their part, and finally summarize the partial results to get the total.
The problem is that the calculation time increases with the number of threads instead of decreasing (i7 processor, Windows 7)
Several people are working on this, and we do not know why this is so. Hope someone gives me some advice. I am attaching the code:
import java.io.File;
import java.io.FileWriter;
import java.io.PrintWriter;
import java.util.ArrayList;
import java.util.concurrent.ConcurrentLinkedQueue;
public class Runner {
private static final int MAXT = 10;
static PrintWriter outM;
static PrintWriter outMTime;
public static void main(String[] args){
double xp = 2;
double xk = 3;
filesOp();
for(int threadNumber=1; threadNumber<=MAXT; threadNumber++){
outM.print("\t"+ threadNumber);
outMTime.print("\t"+ threadNumber);
}
double time1;
double time2;
for(int n=10000; n<=10000000; n=n*10){
System.out.println("Licze dla: " + n + " punktow.");
outM.print("\n"+n);
outMTime.print("\n"+n);
for(int threadNumber=1; threadNumber<=MAXT; threadNumber++){
outM.print("\t");
outMTime.print("\t");
time1=System.nanoTime();
multiThread(xp, xk, n, threadNumber);
time2=System.nanoTime();
outMTime.print((time2-time1)/1000000);
}
}
outM.close();
outMTime.close();
}
public static void multiThread(double xp, double xk, int n, int threadNumber){
ArrayList<Thread> threadList = new ArrayList<Thread>();
ConcurrentLinkedQueue<Double> results = new ConcurrentLinkedQueue<Double>();
for(int i=0; i<threadNumber; i++){
MonteCarlo mc = new MonteCarlo( xp+(i*((xk-xp)/threadNumber)), xp+((i+1)*((xk-xp)/threadNumber)), (int)(n/threadNumber), results);
Thread t = new Thread(mc);
threadList.add(t);
t.start();
}
for(Thread t : threadList){
try {
t.join();
} catch (Exception e) {
e.printStackTrace();
}
}
double wynik = 0;
for(double r: results){
wynik= wynik + r;
}
outM.print(wynik);
}
public static void filesOp(){
File fileTemp;
fileTemp = new File("wyniki.txt");
if (fileTemp.exists()) fileTemp.delete();
fileTemp = new File("pomiary.txt");
if (fileTemp.exists()) fileTemp.delete();
try {
outM = new PrintWriter(new FileWriter("wyniki.txt", true));
outMTime = new PrintWriter(new FileWriter("pomiary.txt", true));
} catch (Exception e) {
e.printStackTrace();
}
}
}
public class MonteCarlo implements Runnable{
double xp;
double xk;
long n;
ConcurrentLinkedQueue<Double> results;
MonteCarlo(double xp, double xk, long n, ConcurrentLinkedQueue<Double> results){
this.xp=xp;
this.xk=xk;
this.n=n;
this.results=results;
}
private static double func(double x) {
return x*x+3;
}
private static double funcIn(double x, double y) {
if (( y > 0) && (y <= func(x)))
return 1;
else if (( y > 0) && (y <= func(x)))
return -1;
return 0;
}
private static double randomPoint(double a, double b) {
return a + Math.random() * (b-a);
}
public void run(){
double yp, yk, calka;
int pointsIn;
yp = 0;
yk = Math.ceil(Math.max(func(xp), func(xk)));
pointsIn = 0;
for (long i=0; i<n; i++) {
pointsIn += funcIn(randomPoint(xp, xk), randomPoint(yp, yk));
}
calka = (pointsIn / (double)n) * ((xk-xp) * (yk-yp));
results.add(calka);
}
}
And an example of the results:
1 2 3 4 5 6 7 8 9 10
10000 6.185818 2.821405 3.721287 3.470309 4.068365 3.604195 4.323075 4.192455 6.159694 4.239105
100000 10.994522 15.874134 34.992323 40.851124 36.199631 49.54579 45.122417 61.427132 55.845435 60.7661
1000000 108.653008 274.443662 340.274574 407.054352 437.455361 469.853467 496.849012 584.519687 571.09329 594.152023
10000000 1066.059033 2877.947652 3600.551966 4175.707089 4488.434247 5081.572093 5501.217804 6374.335759 6128.274553 6339.043475