Java 和两个双[][] 与并行流
2022-09-01 23:07:08
假设我有这两个矩阵：
double[][] a = new double[2][2]
a[0][0] = 1
a[0][1] = 2
a[1][0] = 3
a[1][1] = 4
double[][] b = new double[2][2]
b[0][0] = 1
b[0][1] = 2
b[1][0] = 3
b[1][1] = 4
以传统方式，为了求和这个矩阵，我会做一个嵌套的for循环：
int rows = a.length;
int cols = a[0].length;
double[][] res = new double[rows][cols];
for(int i = 0; i < rows; i++){
for(int j = 0; j < cols; j++){
res[i][j] = a[i][j] + b[i][j];
}
}
我对流API相当陌生，但我认为这很适合使用，所以我的问题是是否有办法做到这一点并利用并行处理？parallelStream
编辑：不确定这是否是正确的地方，但在这里我们去：使用一些建议，我对Stream进行了测试。设置是这样的：经典方法：
public class ClassicMatrix {
private final double[][] components;
private final int cols;
private final int rows;
public ClassicMatrix(final double[][] components){
this.components = components;
this.rows = components.length;
this.cols = components[0].length;
}
public ClassicMatrix addComponents(final ClassicMatrix a) {
final double[][] res = new double[rows][cols];
for (int i = 0; i < rows; i++) {
for (int j = 0; j < rows; j++) {
res[i][j] = components[i][j] + a.components[i][j];
}
}
return new ClassicMatrix(res);
}
}
使用@dkatzel建议：
public class MatrixStream1 {
private final double[][] components;
private final int cols;
private final int rows;
public MatrixStream1(final double[][] components){
this.components = components;
this.rows = components.length;
this.cols = components[0].length;
}
public MatrixStream1 addComponents(final MatrixStream1 a) {
final double[][] res = new double[rows][cols];
IntStream.range(0, rows*cols).parallel().forEach(i -> {
int x = i/rows;
int y = i%rows;
res[x][y] = components[x][y] + a.components[x][y];
});
return new MatrixStream1(res);
}
}
使用@Eugene建议：
public class MatrixStream2 {
private final double[][] components;
private final int cols;
private final int rows;
public MatrixStream2(final double[][] components) {
this.components = components;
this.rows = components.length;
this.cols = components[0].length;
}
public MatrixStream2 addComponents(final MatrixStream2 a) {
final double[][] res = new double[rows][cols];
IntStream.range(0, rows)
.forEach(i -> Arrays.parallelSetAll(res[i], j -> components[i][j] * a.components[i][j]));
return new MatrixStream2(res);
}
}
和一个测试类，为每个方法运行 3 次独立乘以一（只需替换 main（）中的方法名称）：
public class MatrixTest {
private final static String path = "/media/manuel/workspace/data/";
public static void main(String[] args) {
final List<Double[]> lst = new ArrayList<>();
for (int i = 100; i < 8000; i = i + 400) {
final Double[] d = testClassic(i);
System.out.println(d[0] + " : " + d[1]);
lst.add(d);
}
IOUtils.saveToFile(path + "classic.csv", lst);
}
public static Double[] testClassic(final int i) {
final ClassicMatrix a = new ClassicMatrix(rand(i));
final ClassicMatrix b = new ClassicMatrix(rand(i));
final long start = System.currentTimeMillis();
final ClassicMatrix mul = a.addComponents(b);
final long now = System.currentTimeMillis();
final double elapsed = (now - start);
return new Double[] { (double) i, elapsed };
}
public static Double[] testStream1(final int i) {
final MatrixStream1 a = new MatrixStream1(rand(i));
final MatrixStream1 b = new MatrixStream1(rand(i));
final long start = System.currentTimeMillis();
final MatrixStream1 mul = a.addComponents(b);
final long now = System.currentTimeMillis();
final double elapsed = (now - start);
return new Double[] { (double) i, elapsed };
}
public static Double[] testStream2(final int i) {
final MatrixStream2 a = new MatrixStream2(rand(i));
final MatrixStream2 b = new MatrixStream2(rand(i));
final long start = System.currentTimeMillis();
final MatrixStream2 mul = a.addComponents(b);
final long now = System.currentTimeMillis();
final double elapsed = (now - start);
return new Double[] { (double) i, elapsed };
}
private static double[][] rand(final int size) {
final double[][] rnd = new double[size][size];
for (int i = 0; i < size; i++) {
for (int j = 0; j < size; j++) {
rnd[i][j] = Math.random();
}
}
return rnd;
}
}
结果：
Classic Matrix size, Time (ms)
100.0,1.0
500.0,5.0
900.0,5.0
1300.0,43.0
1700.0,94.0
2100.0,26.0
2500.0,33.0
2900.0,46.0
3300.0,265.0
3700.0,71.0
4100.0,87.0
4500.0,380.0
4900.0,432.0
5300.0,215.0
5700.0,238.0
6100.0,577.0
6500.0,677.0
6900.0,609.0
7300.0,584.0
7700.0,592.0
Stream1, Time(ms)
100.0,86.0
500.0,13.0
900.0,9.0
1300.0,47.0
1700.0,92.0
2100.0,29.0
2500.0,33.0
2900.0,46.0
3300.0,253.0
3700.0,71.0
4100.0,90.0
4500.0,352.0
4900.0,373.0
5300.0,497.0
5700.0,485.0
6100.0,579.0
6500.0,711.0
6900.0,800.0
7300.0,780.0
7700.0,902.0
Stream2, Time(ms)
100.0,111.0
500.0,42.0
900.0,12.0
1300.0,54.0
1700.0,97.0
2100.0,110.0
2500.0,177.0
2900.0,71.0
3300.0,250.0
3700.0,106.0
4100.0,359.0
4500.0,143.0
4900.0,233.0
5300.0,261.0
5700.0,289.0
6100.0,406.0
6500.0,814.0
6900.0,830.0
7300.0,828.0
7700.0,911.0
我做了一个情节来更好地比较：
根本没有任何改进。漏洞在哪里？矩阵是否很小 （7700 x 7700）？比这更大，它炸毁了我的电脑内存。
