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我正在做一些关于Java 7中的叉/加入框架的性能研究。为了改善测试结果，我想在测试过程中使用不同的递归算法。其中之一是乘法矩阵。在Java中叉加入矩阵乘法
public class MatrixMultiply {
static final int DEFAULT_GRANULARITY = 16;
/** The quadrant size at which to stop recursing down
* and instead directly multiply the matrices.
* Must be a power of two. Minimum value is 2.
**/
static int granularity = DEFAULT_GRANULARITY;
public static void main(String[] args) {
final String usage = "Usage: java MatrixMultiply <threads> <matrix size (must be a power of two)> [<granularity>] \n Size and granularity must be powers of two.\n For example, try java MatrixMultiply 2 512 16";
try {
int procs;
int n;
try {
procs = Integer.parseInt(args[0]);
n = Integer.parseInt(args[1]);
if (args.length > 2) granularity = Integer.parseInt(args[2]);
}
catch (Exception e) {
System.out.println(usage);
return;
}
if (((n & (n - 1)) != 0) ||
((granularity & (granularity - 1)) != 0) ||
granularity < 2) {
System.out.println(usage);
return;
}
float[][] a = new float[n][n];
float[][] b = new float[n][n];
float[][] c = new float[n][n];
init(a, b, n);
FJTaskRunnerGroup g = new FJTaskRunnerGroup(procs);
g.invoke(new Multiplier(a, 0, 0, b, 0, 0, c, 0, 0, n));
g.stats();
// check(c, n);
}
catch (InterruptedException ex) {}
}
// To simplify checking, fill with all 1's. Answer should be all n's.
static void init(float[][] a, float[][] b, int n) {
for (int i = 0; i < n; ++i) {
for (int j = 0; j < n; ++j) {
a[i][j] = 1.0F;
b[i][j] = 1.0F;
}
}
}
static void check(float[][] c, int n) {
for (int i = 0; i < n; i++) {
for (int j = 0; j < n; j++) {
if (c[i][j] != n) {
throw new Error("Check Failed at [" + i +"]["+j+"]: " + c[i][j]);
}
}
}
}
/**
* Multiply matrices AxB by dividing into quadrants, using algorithm:
* <pre>
* A x B
*
* A11 | A12 B11 | B12 A11*B11 | A11*B12 A12*B21 | A12*B22
* |----+----| x |----+----| = |--------+--------| + |---------+-------|
* A21 | A22 B21 | B21 A21*B11 | A21*B21 A22*B21 | A22*B22
* </pre>
*/
static class Multiplier extends FJTask {
final float[][] A; // Matrix A
final int aRow; // first row of current quadrant of A
final int aCol; // first column of current quadrant of A
final float[][] B; // Similarly for B
final int bRow;
final int bCol;
final float[][] C; // Similarly for result matrix C
final int cRow;
final int cCol;
final int size; // number of elements in current quadrant
Multiplier(float[][] A, int aRow, int aCol,
float[][] B, int bRow, int bCol,
float[][] C, int cRow, int cCol,
int size) {
this.A = A; this.aRow = aRow; this.aCol = aCol;
this.B = B; this.bRow = bRow; this.bCol = bCol;
this.C = C; this.cRow = cRow; this.cCol = cCol;
this.size = size;
}
public void run() {
if (size <= granularity) {
multiplyStride2();
}
else {
int h = size/2;
coInvoke(new FJTask[] {
seq(new Multiplier(A, aRow, aCol, // A11
B, bRow, bCol, // B11
C, cRow, cCol, // C11
h),
new Multiplier(A, aRow, aCol+h, // A12
B, bRow+h, bCol, // B21
C, cRow, cCol, // C11
h)),
seq(new Multiplier(A, aRow, aCol, // A11
B, bRow, bCol+h, // B12
C, cRow, cCol+h, // C12
h),
new Multiplier(A, aRow, aCol+h, // A12
B, bRow+h, bCol+h, // B22
C, cRow, cCol+h, // C12
h)),
seq(new Multiplier(A, aRow+h, aCol, // A21
B, bRow, bCol, // B11
C, cRow+h, cCol, // C21
h),
new Multiplier(A, aRow+h, aCol+h, // A22
B, bRow+h, bCol, // B21
C, cRow+h, cCol, // C21
h)),
seq(new Multiplier(A, aRow+h, aCol, // A21
B, bRow, bCol+h, // B12
C, cRow+h, cCol+h, // C22
h),
new Multiplier(A, aRow+h, aCol+h, // A22
B, bRow+h, bCol+h, // B22
C, cRow+h, cCol+h, // C22
h))
});
}
}
/**
* Version of matrix multiplication that steps 2 rows and columns
* at a time. Adapted from Cilk demos.
* Note that the results are added into C, not just set into C.
* This works well here because Java array elements
* are created with all zero values.
**/
void multiplyStride2() {
for (int j = 0; j < size; j+=2) {
for (int i = 0; i < size; i +=2) {
float[] a0 = A[aRow+i];
float[] a1 = A[aRow+i+1];
float s00 = 0.0F;
float s01 = 0.0F;
float s10 = 0.0F;
float s11 = 0.0F;
for (int k = 0; k < size; k+=2) {
float[] b0 = B[bRow+k];
s00 += a0[aCol+k] * b0[bCol+j];
s10 += a1[aCol+k] * b0[bCol+j];
s01 += a0[aCol+k] * b0[bCol+j+1];
s11 += a1[aCol+k] * b0[bCol+j+1];
float[] b1 = B[bRow+k+1];
s00 += a0[aCol+k+1] * b1[bCol+j];
s10 += a1[aCol+k+1] * b1[bCol+j];
s01 += a0[aCol+k+1] * b1[bCol+j+1];
s11 += a1[aCol+k+1] * b1[bCol+j+1];
}
C[cRow+i] [cCol+j] += s00;
C[cRow+i] [cCol+j+1] += s01;
C[cRow+i+1][cCol+j] += s10;
C[cRow+i+1][cCol+j+1] += s11;
}
}
}
}
}
该代码可用于旧版本的叉的书面/ join框架：
我Doug Lea的网站（）下载下面的例子。所以我必须重写它。我重写的代码实现我自己的接口，看起来像这样：
public class Java7MatrixMultiply implements Algorithm {
private static final int SIZE = 32;
private static final int THRESHOLD = 8;
private float[][] a = new float[SIZE][SIZE];
private float[][] b = new float[SIZE][SIZE];
private float[][] c = new float[SIZE][SIZE];
ForkJoinPool forkJoinPool;
@Override
public void initialize() {
init(a, b, SIZE);
}
@Override
public void execute() {
MatrixMultiplyTask mainTask = new MatrixMultiplyTask(a, 0, 0, b, 0, 0, c, 0, 0, SIZE);
forkJoinPool = new ForkJoinPool();
forkJoinPool.invoke(mainTask);
System.out.println("Terminated!");
}
@Override
public void printResult() {
check(c, SIZE);
for (int i = 0; i < SIZE; i++) {
for (int j = 0; j < SIZE; j++) {
System.out.print(c[i][j] + " ");
}
System.out.println();
}
}
// To simplify checking, fill with all 1's. Answer should be all n's.
static void init(float[][] a, float[][] b, int n) {
for (int i = 0; i < n; ++i) {
for (int j = 0; j < n; ++j) {
a[i][j] = 1.0F;
b[i][j] = 1.0F;
}
}
}
static void check(float[][] c, int n) {
for (int i = 0; i < n; i++) {
for (int j = 0; j < n; j++) {
if (c[i][j] != n) {
//throw new Error("Check Failed at [" + i + "][" + j + "]: " + c[i][j]);
System.out.println("Check Failed at [" + i + "][" + j + "]: " + c[i][j]);
}
}
}
}
private class MatrixMultiplyTask extends RecursiveAction {
private final float[][] A; // Matrix A
private final int aRow; // first row of current quadrant of A
private final int aCol; // first column of current quadrant of A
private final float[][] B; // Similarly for B
private final int bRow;
private final int bCol;
private final float[][] C; // Similarly for result matrix C
private final int cRow;
private final int cCol;
private final int size;
MatrixMultiplyTask(float[][] A, int aRow, int aCol, float[][] B,
int bRow, int bCol, float[][] C, int cRow, int cCol, int size) {
this.A = A;
this.aRow = aRow;
this.aCol = aCol;
this.B = B;
this.bRow = bRow;
this.bCol = bCol;
this.C = C;
this.cRow = cRow;
this.cCol = cCol;
this.size = size;
}
@Override
protected void compute() {
if (size <= THRESHOLD) {
multiplyStride2();
} else {
int h = size/2;
invokeAll(new MatrixMultiplyTask[] {
new MatrixMultiplyTask(A, aRow, aCol, // A11
B, bRow, bCol, // B11
C, cRow, cCol, // C11
h),
new MatrixMultiplyTask(A, aRow, aCol + h, // A12
B, bRow + h, bCol, // B21
C, cRow, cCol, // C11
h),
new MatrixMultiplyTask(A, aRow, aCol, // A11
B, bRow, bCol + h, // B12
C, cRow, cCol + h, // C12
h),
new MatrixMultiplyTask(A, aRow, aCol + h, // A12
B, bRow + h, bCol + h, // B22
C, cRow, cCol + h, // C12
h),
new MatrixMultiplyTask(A, aRow + h, aCol, // A21
B, bRow, bCol, // B11
C, cRow + h, cCol, // C21
h),
new MatrixMultiplyTask(A, aRow + h, aCol + h, // A22
B, bRow + h, bCol, // B21
C, cRow + h, cCol, // C21
h),
new MatrixMultiplyTask(A, aRow + h, aCol, // A21
B, bRow, bCol + h, // B12
C, cRow + h, cCol + h, // C22
h),
new MatrixMultiplyTask(A, aRow + h, aCol + h, // A22
B, bRow + h, bCol + h, // B22
C, cRow + h, cCol + h, // C22
h) });
}
}
/**
* Version of matrix multiplication that steps 2 rows and columns at a
* time. Adapted from Cilk demos. Note that the results are added into
* C, not just set into C. This works well here because Java array
* elements are created with all zero values.
**/
void multiplyStride2() {
for (int j = 0; j < size; j += 2) {
for (int i = 0; i < size; i += 2) {
float[] a0 = A[aRow + i];
float[] a1 = A[aRow + i + 1];
float s00 = 0.0F;
float s01 = 0.0F;
float s10 = 0.0F;
float s11 = 0.0F;
for (int k = 0; k < size; k += 2) {
float[] b0 = B[bRow + k];
s00 += a0[aCol + k] * b0[bCol + j];
s10 += a1[aCol + k] * b0[bCol + j];
s01 += a0[aCol + k] * b0[bCol + j + 1];
s11 += a1[aCol + k] * b0[bCol + j + 1];
float[] b1 = B[bRow + k + 1];
s00 += a0[aCol + k + 1] * b1[bCol + j];
s10 += a1[aCol + k + 1] * b1[bCol + j];
s01 += a0[aCol + k + 1] * b1[bCol + j + 1];
s11 += a1[aCol + k + 1] * b1[bCol + j + 1];
}
C[cRow + i][cCol + j] += s00;
C[cRow + i][cCol + j + 1] += s01;
C[cRow + i + 1][cCol + j] += s10;
C[cRow + i + 1][cCol + j + 1] += s11;
}
}
}
}
}
有时我的计算未能通过检查。矩阵的某些区域具有与预期不同的值。这些不一致是随机的，并不总是会发生。我怀疑计算方法出了问题，因为我不得不重写使用Seq类的部分。 Seq klass按顺序执行任务，与invokeAll（）方法不同。在当前版本的fork/join框架中，该类不再存在。我对矩阵乘法算法不是很熟悉，所以很难看出错误。有什么建议么？
谢谢。它完美无瑕。 – TheArchitect 2011-03-29 13:56:02