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：

/** Copyright (c) 2010, 2013, Oracle and/or its affiliates. All rights reserved.** Redistribution and use in source and binary forms, with or without* modification, are permitted provided that the following conditions* are met:**  - Redistributions of source code must retain the above copyright*   notice, this list of conditions and the following disclaimer.**  - Redistributions in binary form must reproduce the above copyright*   notice, this list of conditions and the following disclaimer in the*   documentation and/or other materials provided with the distribution.**  - Neither the name of Oracle or the names of its*   contributors may be used to endorse or promote products derived*   from this software without specific prior written permission.** THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS* IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO,* THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR* PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR* CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,* EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,* PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR* PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF* LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING* NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS* SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.*/ 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

原标题：JAVA中的Fork/Join框架

关键词：JAVA