看了下Java Tutorials中的fork/join章節，整理下。什麼是fork/join框架 fork/join框架是ExecutorService介面的一個實現，可以幫助開發人員充分利用多核處理器的優勢，編寫出並行執行的程式，提高應用程式的性能；設計的目的是為了處理那些可以被遞歸拆分的任務 ...

看了下Java Tutorials中的fork/join章節，整理下。

什麼是fork/join框架

　　fork/join框架是ExecutorService介面的一個實現，可以幫助開發人員充分利用多核處理器的優勢，編寫出並行執行的程式，提高應用程式的性能；設計的目的是為了處理那些可以被遞歸拆分的任務。

　　fork/join框架與其它ExecutorService的實現類相似，會給線程池中的線程分發任務，不同之處在於它使用了工作竊取演算法，所謂工作竊取，指的是對那些處理完自身任務的線程，會從其它線程竊取任務執行。

　　fork/join框架的核心是ForkJoinPool類，該類繼承了AbstractExecutorService類。ForkJoinPool實現了工作竊取演算法並且能夠執行 ForkJoinTask任務。

基本使用方法

　　在使用fork/join框架之前，我們需要先對任務進行分割，任務分割代碼應該跟下麵的偽代碼類似：

if (任務足夠小){
  直接執行該任務;
}else{
  將任務一分為二;
  執行這兩個任務並等待結果;
}

　　首先，我們會在ForkJoinTask的子類中封裝以上代碼，不過一般我們會使用更加具體的ForkJoinTask類型，如 RecursiveTask（可以返回一個結果）或RecursiveAction。

　　當寫好ForkJoinTask的子類後，創建該對象，該對象代表了所有需要完成的任務；然後將這個任務對象傳給ForkJoinPool實例的invoke()去執行即可。

例子-圖像模糊

　　為了更加直觀的理解fork/join框架是如何工作的，可以看一下下麵這個例子。假定我們有一個圖像模糊的任務需要完成，原始圖像數據可以用一個整型數組表示，每一個整型元素包含了一個像素點的顏色值（RBG，存放在整型元素的不同位中）。目標圖像同樣是由一個整型數組構成，每個整型元素包含RBG顏色信息；

　　執行模糊操作需要遍歷原始圖像整型數組的每個元素，並對其周圍的像素點做均值操作（RGB均值），然後將結果存放到目標數組中。由於圖像是一個大數組，這個處理操作會花費一定的時間。但是有了fork/join框架，我們可以充分利用多核處理器進行並行計算。如下是一個可能的代碼實現（圖像做水平方向的模糊操作）：

Tips：該例子僅僅是闡述fork/join框架的使用，並不推薦使用該方法做圖像模糊，圖像邊緣處理也沒做判斷

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;
        }
    }
...

　　現在，我們開始編寫compute()的實現方法，該方法分成兩部分：直接執行模糊操作和任務的劃分；一個數組長度閾值sThreshold可以幫助我們決定任務是直接執行還是進行劃分；

    @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));
    }

接下來按如下步驟即可完成圖像模糊任務啦：

1、創建圖像模糊任務

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

2、創建ForkJoinPool

ForkJoinPool pool = new ForkJoinPool();

3、執行圖像模糊任務

pool.invoke(fb);

完整代碼如下：

/*
* 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:
*
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*     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;
    }
}

View Code

測試了一下，執行效果如下：

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

　　除了我們上面提到的使用fork/join框架並行執行圖像模糊任務之外，在JAVA SE中，也已經利用fork/join框架實現了一些非常有用的特性。其中一個實現是在JAVA SE8 中java.util.Arrays 類的parallelSort()方法。這些方法和sort()方法類似，但是可以通過fork/join框架並行執行。對於大數組排序，在多核處理器系統中，使用並行排序方法比順序排序更加高效。當然，關於這些排序方法是如何利用fork/join框架不在本篇文章討論範圍，更多信息可以查看JAVA API文檔。
　　另一個fork/join框架的實現是在JAVA SE8中的java.util.streams包內，與Lambda表達式相關，更多信息，可以查看https://docs.oracle.com/javase/tutorial/java/javaOO/lambdaexpressions.html鏈接。

參考鏈接：https://docs.oracle.com/javase/tutorial/essential/concurrency/forkjoin.html

JAVA中的Fork/Join框架

什麼是fork/join框架

基本使用方法

例子-圖像模糊

Source image: C:\test6.jpgArray size is 120000Threshold is 100004 processors are availableImage blur took 10 milliseconds.Output image: C:\test6_out.jpg

JDK中使用fork/join的例子

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