JDK7并行计算框架介绍一 Fork/Join概述（官方原版英文）

Fork/Join

New in the Java SE 7 release, the fork/join framework is an implementation of the ExecutorService interface that helps you take advantage of multiple processors. It is designed for work that can be broken into smaller pieces recursively. The goal is to use all the available processing power to enhance the performance of your application.

As with any ExecutorService, the fork/join framework distributes tasks to worker threads in a thread pool. The fork/join framework is distinct because it uses a work-stealing algorithm. Worker threads that run out of things to do can steal tasks from other threads that are still busy.

The center of the fork/join framework is the ForkJoinPool class, an extension of AbstractExecutorService. ForkJoinPool implements the core work-stealing algorithm and can execute ForkJoinTasks.

Basic Use

Using the fork/join framework is simple. The first step is to write some code that performs a segment of the work. Your code should look similar to this:

if (my portion of the work is small enough)
  do the work directly
else
  split my work into two pieces
  invoke the two pieces and wait for the results

Wrap this code as a ForkJoinTask subclass, typically as one of its more specialized types RecursiveTask(which can return a result) or RecursiveAction.

After your ForkJoinTask is ready, create one that represents all the work to be done and pass it to the invoke() method of a ForkJoinPool instance.

Blurring for Clarity

To help you understand how the fork/join framework works, consider a simple example. Suppose you want to perform a simple blur on an image. The original source image is represented by an array of integers, where each integer contains the color values for a single pixel. The blurred destination image is also represented by an integer array with the same size as the source.

Performing the blur is accomplished by working through the source array one pixel at a time. Each pixel is averaged with its surrounding pixels (the red, green, and blue components are averaged), and the result is placed in the destination array. Here is one possible implementation:

public class ForkBlur extends RecursiveAction {
    private int[] mSource;
    private int mStart;
    private int mLength;
    private int[] mDestination;
  
    // Processing window size, should be odd.
    private int mBlurWidth = 15;
  
    public ForkBlur(int[] src, int start, int length, int[] dst) {
        mSource = src;
        mStart = start;
        mLength = length;
        mDestination = dst;
    }

    protected void computeDirectly() {
        int sidePixels = (mBlurWidth - 1) / 2;
        for (int index = mStart; index < mStart + mLength; index++) {
            // Calculate average.
            float rt = 0, gt = 0, bt = 0;
            for (int mi = -sidePixels; mi <= sidePixels; mi++) {
                int mindex = Math.min(Math.max(mi + index, 0),
                                    mSource.length - 1);
                int pixel = mSource[mindex];
                rt += (float)((pixel & 0x00ff0000) >> 16)
                      / mBlurWidth;
                gt += (float)((pixel & 0x0000ff00) >>  8)
                      / mBlurWidth;
                bt += (float)((pixel & 0x000000ff) >>  0)
                      / mBlurWidth;
            }
          
            // Re-assemble destination pixel.
            int dpixel = (0xff000000     ) |
                   (((int)rt) << 16) |
                   (((int)gt) <<  8) |
                   (((int)bt) <<  0);
            mDestination[index] = dpixel;
        }
    }
  
  ...

Now you implement the abstract compute() method, which either performs the blur directly or splits it into two smaller tasks. A simple array length threshold helps determine whether the work is performed or split.

protected static int sThreshold = 100000;

protected void compute() {
    if (mLength < sThreshold) {
        computeDirectly();
        return;
    }
    
    int split = mLength / 2;
    
    invokeAll(new ForkBlur(mSource, mStart, split, mDestination),
              new ForkBlur(mSource, mStart + split, mLength - split,
                           mDestination));
}

If the previous methods are in a subclass of the RecursiveAction class, setting it up to run in a ForkJoinPool is straightforward.

Create a task that represents all of the work to be done.

// source image pixels are in src
// destination image pixels are in dst
ForkBlur fb = new ForkBlur(src, 0, src.length, dst);

Create the ForkJoinPool that will run the task.

ForkJoinPool pool = new ForkJoinPool();

Run the task.

pool.invoke(fb);

For the full source code, including some extra code that shows the source and destination images in windows, see the ForkBlur class.

官网地址：http://gee.cs.oswego.edu/dl/concurrency-interest/