在java中并行化任务的最简单方法是什么？ [英] What is the easiest way to parallelize a task in java?

问题描述

说我有类似的任务：

for(Object object: objects) {
    Result result = compute(objects);
    list.add(result);
}

并行化每个compute（）的最简单方法是什么（假设它们已经是可并行化）？

What is the easiest way to parallelize each compute() (assuming they are already parallelizable)?

我不需要一个严格符合上述代码的答案，只是一般答案。但是如果您需要更多信息：我的任务是IO绑定的，这是针对Spring Web应用程序的，任务将在HTTP请求中执行。

I do not need an answer that matches strictly the code above, just a general answer. But if you need more info: my tasks are IO bound and this is for a Spring Web application and the tasks are going to be executed in a HTTP request.

推荐答案

我建议看看 ExecutorService 。

特别是这样的事情：

ExecutorService EXEC = Executors.newCachedThreadPool();
List<Callable<Result>> tasks = new ArrayList<Callable<Result>>();
for (final Object object: objects) {
    Callable<Result> c = new Callable<Result>() {
        @Override
        public Result call() throws Exception {
            return compute(object);
        }
    };
    tasks.add(c);
}
List<Future<Result>> results = EXEC.invokeAll(tasks);

请注意，使用 newCachedThreadPool 可能会很糟糕 objects 是一个很重要的列表。缓存的线程池可以为每个任务创建一个线程！你可能想要使用 newFixedThreadPool（n）其中n是合理的（比如你拥有的核心数，假设 compute（）是CPU绑定的。）

Note that using newCachedThreadPool could be bad if objects is a big list. A cached thread pool could create a thread per task! You may want to use newFixedThreadPool(n) where n is something reasonable (like the number of cores you have, assuming compute() is CPU bound).

这是实际运行的完整代码：

Here's full code that actually runs:

import java.util.ArrayList;
import java.util.List;
import java.util.Random;
import java.util.concurrent.Callable;
import java.util.concurrent.ExecutionException;
import java.util.concurrent.ExecutorService;
import java.util.concurrent.Executors;
import java.util.concurrent.Future;

public class ExecutorServiceExample {
    private static final Random PRNG = new Random();

    private static class Result {
        private final int wait;
        public Result(int code) {
            this.wait = code;
        }
    }

    public static Result compute(Object obj) throws InterruptedException {
        int wait = PRNG.nextInt(3000);
        Thread.sleep(wait);
        return new Result(wait);
    }

    public static void main(String[] args) throws InterruptedException,
        ExecutionException {
        List<Object> objects = new ArrayList<Object>();
        for (int i = 0; i < 100; i++) {
            objects.add(new Object());
        }

        List<Callable<Result>> tasks = new ArrayList<Callable<Result>>();
        for (final Object object : objects) {
            Callable<Result> c = new Callable<Result>() {
                @Override
                public Result call() throws Exception {
                    return compute(object);
                }
            };
            tasks.add(c);
        }

        ExecutorService exec = Executors.newCachedThreadPool();
        // some other exectuors you could try to see the different behaviours
        // ExecutorService exec = Executors.newFixedThreadPool(3);
        // ExecutorService exec = Executors.newSingleThreadExecutor();
        try {
            long start = System.currentTimeMillis();
            List<Future<Result>> results = exec.invokeAll(tasks);
            int sum = 0;
            for (Future<Result> fr : results) {
                sum += fr.get().wait;
                System.out.println(String.format("Task waited %d ms",
                    fr.get().wait));
            }
            long elapsed = System.currentTimeMillis() - start;
            System.out.println(String.format("Elapsed time: %d ms", elapsed));
            System.out.println(String.format("... but compute tasks waited for total of %d ms; speed-up of %.2fx", sum, sum / (elapsed * 1d)));
        } finally {
            exec.shutdown();
        }
    }
}

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