How does Java implement parallel computing?

Parallel computing in Java is implemented by distributing tasks to multiple execution threads or processes, which can significantly speed up applications. The main API is the java.util.concurrent package, which includes threads, thread pools, and concurrency classes to optimize concurrent access. Practical cases, such as parallel summation, make full use of multi-core processors by decomposing tasks and executing subtasks asynchronously, greatly improving computing efficiency.

How to implement parallel computing in Java

In modern computing, parallel computing has become accelerating applications using multi-core CPUs and GPUs necessary technology. Java provides a rich API that enables developers to easily benefit from parallel computing.

Basic Concepts

Parallel computing involves assigning tasks to multiple execution threads or processes. In this way, tasks can be executed simultaneously, shortening the overall execution time. The main parallel API in Java is the java.util.concurrent package.

Threads

Threads are lightweight execution units that share the memory space of an application. By creating and starting threads, you can execute tasks in parallel.

// 创建一个线程
Thread thread = new Thread(() -> {
    // 要执行的任务
});

// 启动线程
thread.start();

Thread Pool

The thread pool manages a collection of threads and automatically creates and destroys threads as needed. This helps improve performance and reduce resource consumption.

// 创建一个线程池
ExecutorService executorService = Executors.newFixedThreadPool(4);

// 提交任务到线程池
executorService.submit(() -> {
    // 要执行的任务
});

// 优雅地关闭线程池
executorService.shutdown();

Concurrency classes

Java also provides concurrency classes such as ConcurrentHashMap and BlockingQueue, which are already provided for parallel access Optimized.

// 创建一个并发 HashMap
ConcurrentHashMap<String, Integer> map = new ConcurrentHashMap<>();

// 插入数据到 HashMap
map.put("key", 10);

// 获取数据从 HashMap
int value = map.get("key");

Practical Case

Consider the following case of parallel summation:

public class SumArrayParallel {

    public static void main(String[] args) {
        int[] numbers = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10};

        // 使用 ForkJoinPool 分解任务
        ForkJoinPool pool = ForkJoinPool.commonPool();
        int sum = pool.invoke(new SumArrayTask(numbers, 0, numbers.length));

        System.out.println("The sum of the array is: " + sum);
    }

    private static class SumArrayTask extends RecursiveTask<Integer> {

        private int[] numbers;
        private int start;
        private int end;

        public SumArrayTask(int[] numbers, int start, int end) {
            this.numbers = numbers;
            this.start = start;
            this.end = end;
        }

        @Override
        protected Integer compute() {
            int sum = 0;

            // 判断任务是否足够小，直接计算
            if (end - start <= 3) {
                for (int i = start; i < end; i++) {
                    sum += numbers[i];
                }
                return sum;
            }

            // 如果任务太大，则分解它
            int mid = (start + end) / 2;
            SumArrayTask leftTask = new SumArrayTask(numbers, start, mid);
            SumArrayTask rightTask = new SumArrayTask(numbers, mid, end);

            // 异步执行子任务
            leftTask.fork();
            rightTask.fork();

            // 等待子任务完成并合并结果
            return leftTask.join() + rightTask.join();
        }
    }
}

In this case, we break the array into smaller chunks , and use ForkJoinPool for asynchronous parallel summation. This approach takes full advantage of multi-core processors and significantly speeds up the summing process of large arrays.

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