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.

// 創(chuàng)建一個(gè)線程
Thread thread = new Thread(() -> {
    // 要執(zhí)行的任務(wù)
});

// 啟動(dòng)線程
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.

// 創(chuàng)建一個(gè)線程池
ExecutorService executorService = Executors.newFixedThreadPool(4);

// 提交任務(wù)到線程池
executorService.submit(() -> {
    // 要執(zhí)行的任務(wù)
});

// 優(yōu)雅地關(guān)閉線程池
executorService.shutdown();

Concurrency classes

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

// 創(chuàng)建一個(gè)并發(fā) HashMap
ConcurrentHashMap<String, Integer> map = new ConcurrentHashMap<>();

// 插入數(shù)據(jù)到 HashMap
map.put("key", 10);

// 獲取數(shù)據(jù)從 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 分解任務(wù)
        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;

            // 判斷任務(wù)是否足夠小，直接計(jì)算
            if (end - start <= 3) {
                for (int i = start; i < end; i++) {
                    sum += numbers[i];
                }
                return sum;
            }

            // 如果任務(wù)太大，則分解它
            int mid = (start + end) / 2;
            SumArrayTask leftTask = new SumArrayTask(numbers, start, mid);
            SumArrayTask rightTask = new SumArrayTask(numbers, mid, end);

            // 異步執(zhí)行子任務(wù)
            leftTask.fork();
            rightTask.fork();

            // 等待子任務(wù)完成并合并結(jié)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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