Programowanie współbieżne w języku Java

Mnożenie macierzy – trzy wersje (książkowa – naiwna, z biblioteki Jama, wielowątkowa).
package mmold;
import java.util.concurrent.ExecutorService;
import java.util.concurrent.Executors;
import java.util.concurrent.TimeUnit;
public class MatrixMultiply02 {
public static void main(String[] args) {
int m = 6;
int n = 7;
int k = 8;
double[][] a = new double[m][n];
double[][] b = new double[n][k];
double[][] c = new double[m][k];
for (int i = 0; i < m; ++i) {
for (int j = 0; j < n; ++j) {
a[i][j] = 1.0D;
}
}
for (int i = 0; i < n; ++i) {
for (int j = 0; j < k; ++j) {
b[i][j] = 1.0D;
}
}
System.out.println("=== First (naive method) test ===");
printMatrix(a, m, n, "A");
printMatrix(b, n, k, "B");
naiveMatrixMultiply(a, b, c);
printMatrix(c, m, k, "C");
System.out.println("=== Second (jama method) test ===");
printMatrix(a, m, n, "A");
printMatrix(b, n, k, "B");
jamaMatrixMultiply(a, b, c);
printMatrix(c, m, k, "C");
}
System.out.println("=== Third (threaded - Executors) test ===");
printMatrix(a, m, n, "A");
printMatrix(b, n, k, "B");
threadedMatrixMultiply(a, b, c, 2);
printMatrix(c, m, k, "C");
// book like method matrix multiplication - matrix indexing is very slow
static void naiveMatrixMultiply(final double[][] a, final double[][] b,
final double[][] c) {
// check(a,b,c); // checks for null args, incorrectly sized matrices,
etc.
final int m = a.length;
final int n = b.length;
final int p = b[0].length;
for (int j = 0; j < p; j++) {
for (int i = 0; i < m; i++) {
double s = 0;
for (int k = 0; k < n; k++) {
s += a[i][k] * b[k][j];
}
c[i][j] = s;
}
}
}
// Jama multiplication - much faster vector indexing than double array
indexing
static void jamaMatrixMultiply(final double[][] a, final double[][] b,
Lab. „Programowanie współbieżne w języku Java”
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