线程池工作窃取实例

摘要：序本文主要来展示一下简版的线程池的实现。默认提供了几个工厂方法思路主要用到的是双端队列，不过这里我们粗糙的实现的话，也可以不用到。测试实例输出从数据来看，还是相对均匀的。

本文主要来展示一下简版的work stealing线程池的实现。

Executors默认提供了几个工厂方法

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/**
     * Creates a thread pool that maintains enough threads to support
     * the given parallelism level, and may use multiple queues to
     * reduce contention. The parallelism level corresponds to the
     * maximum number of threads actively engaged in, or available to
     * engage in, task processing. The actual number of threads may
     * grow and shrink dynamically. A work-stealing pool makes no
     * guarantees about the order in which submitted tasks are
     * executed.
     *
     * @param parallelism the targeted parallelism level
     * @return the newly created thread pool
     * @throws IllegalArgumentException if {@code parallelism <= 0}
     * @since 1.8
     */
    public static ExecutorService newWorkStealingPool(int parallelism) {
        return new ForkJoinPool
            (parallelism,
             ForkJoinPool.defaultForkJoinWorkerThreadFactory,
             null, true);
    }
    /**
     * Creates a work-stealing thread pool using all
     * {@link Runtime#availableProcessors available processors}
     * as its target parallelism level.
     * @return the newly created thread pool
     * @see #newWorkStealingPool(int)
     * @since 1.8
     */
    public static ExecutorService newWorkStealingPool() {
        return new ForkJoinPool
            (Runtime.getRuntime().availableProcessors(),
             ForkJoinPool.defaultForkJoinWorkerThreadFactory,
             null, true);
    }

ForkJoinPool主要用到的是双端队列，不过这里我们粗糙的实现的话，也可以不用到deque。

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public class WorkStealingChannel {
    private static final Logger LOGGER = LoggerFactory.getLogger(WorkStealingChannel.class);
    BlockingDeque[] managedQueues;
    AtomicLongMap stat = AtomicLongMap.create();
    public WorkStealingChannel() {
        int nCPU = Runtime.getRuntime().availableProcessors();
        int queueCount = nCPU / 2 + 1;
        managedQueues = new LinkedBlockingDeque[queueCount];
        for(int i=0;i();
        }
    }
    public void put(T item) throws InterruptedException {
        int targetIndex = Math.abs(item.hashCode() % managedQueues.length);
        BlockingQueue targetQueue = managedQueues[targetIndex];
        targetQueue.put(item);
    }
    public T take() throws InterruptedException {
        int rdnIdx = ThreadLocalRandom.current().nextInt(managedQueues.length);
        int idx = rdnIdx;
        while (true){
            idx = idx % managedQueues.length;
            T item = null;
            if(idx == rdnIdx){
                item = managedQueues[idx].poll();
            }else{
                item = managedQueues[idx].pollLast();
            }
            if(item != null){
                LOGGER.info("take ele from queue {}",idx);
                stat.addAndGet(idx,1);
                return item;
            }
            idx++;
            if(idx == rdnIdx){
                break;
            }
        }
        //走完一轮没有,则随机取一个等待
        LOGGER.info("wait for queue:{}",rdnIdx);
        stat.addAndGet(rdnIdx,1);
        return managedQueues[rdnIdx].take();
    }
    public AtomicLongMap getStat() {
        return stat;
    }
}

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这里根据cpu的数量建立了几个deque，然后每次put的时候，根据hashcode取模放到对应的队列。然后获取的时候，先从随机一个队列取，没有的话，再robbin round取其他队列的，还没有的话，则阻塞等待指定队列的元素。

测试实例

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public class WorkStealingDemo {
    static final WorkStealingChannel channel = new WorkStealingChannel<>();
    static volatile boolean running = true;
    static class Producer extends Thread{
        @Override
        public void run() {
            while(running){
                try {
                    channel.put(UUID.randomUUID().toString());
                } catch (InterruptedException e) {
                    e.printStackTrace();
                }
            }
        }
    }
    static class Consumer extends Thread{
        @Override
        public void run() {
            while(running){
                try {
                    String value = channel.take();
                    System.out.println(value);
                } catch (InterruptedException e) {
                    e.printStackTrace();
                }
            }
        }
    }
    public static void stop(){
        running = false;
        System.out.println(channel.getStat());
    }
    public static void main(String[] args) throws InterruptedException {
        int nCPU = Runtime.getRuntime().availableProcessors();
        int consumerCount = nCPU / 2 + 1;
        for (int i = 0; i < nCPU; i++) {
            new Producer().start();
        }
        for (int i = 0; i < consumerCount; i++) {
            new Consumer().start();
        }
        Thread.sleep(30*1000);
        stop();
    }
}

输出

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{0=660972, 1=660613, 2=661537, 3=659846, 4=659918}

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从数据来看，还是相对均匀的。