I’m doing some performance research on the fork/join framework in Java 7. To improve the test results I want to use different recursive algorithms during the tests. One of them is multiplying matrixes.
I downloaded the following example from Doug Lea's website ():
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;
}
}
}
}
}
This code is written for an older version of the fork/join framework. So I have to rewrite it. My rewritten code implements my own interface and looks like this:
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;
}
}
}
}
}
Sometimes my computation fails to pass the check. Some fields of the Matrix have a different value as expected. These inconsistencies are random, and don’t always occur. I suspect something goes wrong in the compute method, because I had to rewrite the parts where the Seq class is used. The Seq klass executes tasks in order, unlike the invokeAll() method. The class does not exist anymore in the current version of the fork/join framework. I am not very familiar with the matrix multiplication algorithm, so its very hard to see what goes wrong. Any suggestions?
As you already noticed, sequential execution of subtasks that belong to the same quadrant is important for this algorithm. So, you need to implement your own seq() function, for example, as follows, and use it as in original code:
public ForkJoinTask<?> seq(final ForkJoinTask<?> a, final ForkJoinTask<?> b) {
return adapt(new Runnable() {
public void run() {
a.invoke();
b.invoke();
}
});
}
You are accumulating the results in C[cRow + i][cCol + j] += s00; and the like. This is not a thread safe operation so you must synchronize the row or ensure that only one task ever updates the cell. Without this you will see random cells being set incorrectly.
I would check you get the right answer with a concurrency of 1.
BTW: float may not be the best choice here. It has a fairly low number of digits of precision and in heavy matrix operations (which I assume you are doing or there wouldn't be much point using multiple threads) the rounding error can use up most or all your precision. I would suggest considering double instead.
e.g. float has about 7 digits of precision and one rule of thumb is that the error is proportional to the number of calculations. So for a 1K x 1K matrix you might have 4 digits of precision left. For 10K x 10K you might only have three at best. double has 16 digits of precision meaning you may have 12 digits of precision after a 10K x 10K mutlipication.
来源：https://stackoverflow.com/questions/5472744/fork-join-matrix-multiplication-in-java