/*
This file is part of JOP, the Java Optimized Processor
see <http://www.jopdesign.com/>
Copyright (C) 2010, Martin Schoeberl (martin@jopdesign.com)
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see <http://www.gnu.org/licenses/>.
*/
package jembench.parallel;
import jembench.EnumeratedParallelBenchmark;
/**
* Matrix multiplication is very easy to parallelize.
*
* @author martin
*
*/
public class MatrixMul extends EnumeratedParallelBenchmark {
final static int N = 20;
private int[][] arrayA;
private int[][] arrayB;
private int[][] arrayC;
public static int rowCounter = 0;
public static int endCalculation = 0;
public MatrixMul() {
arrayA = new int[N][N];
arrayB = new int[N][N];
arrayC = new int[N][N];
int val = 0;
// set some values in the source matrices
for (int i=0; i<N; ++i) {
for (int j=0; j<N; ++j) {
arrayA[i][j] = val;
val += 12345;
arrayB[i][j] = val;
val += 67890;
}
}
}
public String toString() {
return "matrix multiplication";
}
/**
* Here comes the workload. Do one vector multiplication.
*/
public void executeUnit(int nr) {
int i, j, val;
int[] colB;
for (i = 0; i < N; i++) { // column
val = 0;
colB = arrayB[i];
for (j = 0; j < N; j++) {
val += arrayA[j][nr] * colB[j];
}
arrayC[i][nr] = val;
}
}
/**
* return number of independent tasks
*/
public int getNrOfUnits() {
return N;
}
}