JAVA中的Fork/Join框架

看了下Java Tutorials中的fork/join章节，整理下。

什么是fork/join框架

　　fork/join框架是ExecutorService接口的一个实现，可以帮助开发人员充分利用多核处理器的优势，编写出并行执行的程序，提高应用程序的性能；设计的目的是为了处理那些可以被递归拆分的任务。

　　fork/join框架与其它ExecutorService的实现类相似，会给线程池中的线程分发任务，不同之处在于它使用了工作窃取算法，所谓工作窃取，指的是对那些处理完自身任务的线程，会从其它线程窃取任务执行。

　　fork/join框架的核心是ForkJoinPool类，该类继承了AbstractExecutorService类。ForkJoinPool实现了工作窃取算法并且能够执行 ForkJoinTask任务。

基本使用方法

　　在使用fork/join框架之前，我们需要先对任务进行分割，任务分割代码应该跟下面的伪代码类似：

if (任务足够小){
  直接执行该任务;
}else{
  将任务一分为二;
  执行这两个任务并等待结果;
}

　　首先，我们会在ForkJoinTask的子类中封装以上代码，不过一般我们会使用更加具体的ForkJoinTask类型，如 RecursiveTask（可以返回一个结果）或RecursiveAction。

　　当写好ForkJoinTask的子类后，创建该对象，该对象代表了所有需要完成的任务；然后将这个任务对象传给ForkJoinPool实例的invoke()去执行即可。

例子-图像模糊

　　为了更加直观的理解fork/join框架是如何工作的，可以看一下下面这个例子。假定我们有一个图像模糊的任务需要完成，原始图像数据可以用一个整型数组表示，每一个整型元素包含了一个像素点的颜色值（RBG，存放在整型元素的不同位中）。目标图像同样是由一个整型数组构成，每个整型元素包含RBG颜色信息；

　　执行模糊操作需要遍历原始图像整型数组的每个元素，并对其周围的像素点做均值操作（RGB均值），然后将结果存放到目标数组中。由于图像是一个大数组，这个处理操作会花费一定的时间。但是有了fork/join框架，我们可以充分利用多核处理器进行并行计算。如下是一个可能的代码实现（图像做水平方向的模糊操作）：

Tips：该例子仅仅是阐述fork/join框架的使用，并不推荐使用该方法做图像模糊，图像边缘处理也没做判断

public class ForkBlur extends RecursiveAction {
    private static final long serialVersionUID = -8032915917030559798L;
    private int[] mSource;
    private int mStart;
    private int mLength;
    private int[] mDestination;
    private int mBlurWidth = 15; // Processing window size, should be odd.

    public ForkBlur(int[] src, int start, int length, int[] dst) {
        mSource = src;
        mStart = start;
        mLength = length;
        mDestination = dst;
    }

    // Average pixels from source, write results into destination.
    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;
        }
    }
...

　　现在，我们开始编写compute()的实现方法，该方法分成两部分：直接执行模糊操作和任务的划分；一个数组长度阈值sThreshold可以帮助我们决定任务是直接执行还是进行划分；

    @Override
    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));
    }

接下来按如下步骤即可完成图像模糊任务啦：

1、创建图像模糊任务

ForkBlur fb = new ForkBlur(src, 0, src.length, dst);

2、创建ForkJoinPool

ForkJoinPool pool = new ForkJoinPool();

3、执行图像模糊任务

pool.invoke(fb);

完整代码如下：

/*
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* modification, are permitted provided that the following conditions
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*     from this software without specific prior written permission.
*
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*/

import java.awt.image.BufferedImage;
import java.io.File;
import java.util.concurrent.ForkJoinPool;
import java.util.concurrent.RecursiveAction;
import javax.imageio.ImageIO;

/**
 * ForkBlur implements a simple horizontal image blur. It averages pixels in the
 * source array and writes them to a destination array. The sThreshold value
 * determines whether the blurring will be performed directly or split into two
 * tasks.
 *
 * This is not the recommended way to blur images; it is only intended to
 * illustrate the use of the Fork/Join framework.
 */
public class ForkBlur extends RecursiveAction {
    private static final long serialVersionUID = -8032915917030559798L;
    private int[] mSource;
    private int mStart;
    private int mLength;
    private int[] mDestination;
    private int mBlurWidth = 15; // Processing window size, should be odd.

    public ForkBlur(int[] src, int start, int length, int[] dst) {
        mSource = src;
        mStart = start;
        mLength = length;
        mDestination = dst;
    }

    // Average pixels from source, write results into destination.
    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;
        }
    }
    protected static int sThreshold = 10000;

    @Override
    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));
    }

    // Plumbing follows.
    public static void main(String[] args) throws Exception {
        String srcName = "C:\\test6.jpg";
        File srcFile = new File(srcName);
        BufferedImage image = ImageIO.read(srcFile);

        System.out.println("Source image: " + srcName);

        BufferedImage blurredImage = blur(image);

        String dstName = "C:\\test6_out.jpg";
        File dstFile = new File(dstName);
        ImageIO.write(blurredImage, "jpg", dstFile);

        System.out.println("Output image: " + dstName);

    }

    public static BufferedImage blur(BufferedImage srcImage) {
        int w = srcImage.getWidth();
        int h = srcImage.getHeight();

        int[] src = srcImage.getRGB(0, 0, w, h, null, 0, w);
        int[] dst = new int[src.length];

        System.out.println("Array size is " + src.length);
        System.out.println("Threshold is " + sThreshold);

        int processors = Runtime.getRuntime().availableProcessors();
        System.out.println(Integer.toString(processors) + " processor"
                + (processors != 1 ? "s are " : " is ")
                + "available");

        ForkBlur fb = new ForkBlur(src, 0, src.length, dst);

        ForkJoinPool pool = new ForkJoinPool();

        long startTime = System.currentTimeMillis();
        pool.invoke(fb);
        long endTime = System.currentTimeMillis();

        System.out.println("Image blur took " + (endTime - startTime) +
                " milliseconds.");

        BufferedImage dstImage =
                new BufferedImage(w, h, BufferedImage.TYPE_INT_ARGB);
        dstImage.setRGB(0, 0, w, h, dst, 0, w);

        return dstImage;
    }
}

测试了一下，执行效果如下：

Source image: C:\test6.jpg
Array size is 120000
Threshold is 10000
4 processors are available
Image blur took 10 milliseconds.
Output image: C:\test6_out.jpg

JDK中使用fork/join的例子

　　除了我们上面提到的使用fork/join框架并行执行图像模糊任务之外，在JAVA SE中，也已经利用fork/join框架实现了一些非常有用的特性。其中一个实现是在JAVA SE8 中java.util.Arrays 类的parallelSort()方法。这些方法和sort()方法类似，但是可以通过fork/join框架并行执行。对于大数组排序，在多核处理器系统中，使用并行排序方法比顺序排序更加高效。当然，关于这些排序方法是如何利用fork/join框架不在本篇文章讨论范围，更多信息可以查看JAVA API文档。
　　另一个fork/join框架的实现是在JAVA SE8中的java.util.streams包内，与Lambda表达式相关，更多信息，可以查看https://docs.oracle.com/javase/tutorial/java/javaOO/lambdaexpressions.html链接。

参考链接：https://docs.oracle.com/javase/tutorial/essential/concurrency/forkjoin.html