ForkJoinTask实现斐波那契数列计算

  public static void main(String[] args) throws Exception {
        
        DateTimeFormatter formatter = DateTimeFormatter.ofPattern("yyyy-MM-dd HH:mm:ss");
        System.out.println("主线程开始 " + LocalDateTime.now().format(formatter));
       
        Long invoke = new ForkJoinPool(100).invoke(new Fibonacci(1000));
        System.out.println(invoke);

        System.out.println("主线程结束 " + LocalDateTime.now().format(formatter));
    }

    static ConcurrentMap<Long, Long> concurrentMap = new ConcurrentSkipListMap<>();

    static class Fibonacci extends RecursiveTask<Long> {
        final long n;


        Fibonacci(long n) {
            this.n = n;
        }

        protected Long compute() {
            if (n <= 1)
                return n;
            long n1 = this.n - 1;
            Fibonacci f1 = new Fibonacci(n1);
            f1.fork();
            long n2 = this.n - 2;
            Fibonacci f2 = new Fibonacci(n2);

            //缓存优化
            Long compute2 = concurrentMap.compute(n2, (key, oldValue) -> {
                if (Objects.nonNull(oldValue)) {
                    //存在
                    //直接响应
                    return oldValue;
                }
                //不存在
                //计算
                return f2.compute();
            });
            Long compute1 = concurrentMap.compute(n1, (key, oldValue) -> {
                if (Objects.nonNull(oldValue)) {
                    //存在
                    //直接响应
                    return oldValue;
                }
                //不存在
                //计算
                return f1.join();
            });
            return compute2 + compute1;

            //正常计算
            //return f2.compute()+ f1.join();
        }
    }

主线程开始 2021-08-10 12:39:48
817770325994397771
主线程结束 2021-08-10 12:39:48

1000次斐波那契数列如果是正常计算需要很久很久很久,缓存优化后1s内解决

参考资料:
斐波那契数列切入动态规划