<p>For a classic example, here is a task computing Fibonacci numbers:
<pre> {@code
class Fibonacci extends RecursiveTask<Integer> {
final int n;
Fibonacci(int n) { this.n = n; }
protected Integer compute() {
if (n <= 1)
return n;
Fibonacci f1 = new Fibonacci(n - 1);
f1.fork();
Fibonacci f2 = new Fibonacci(n - 2);
return f2.compute() + f1.join();
}
}}</pre>
However, besides being a dumb way to compute Fibonacci functions
(there is a simple fast linear algorithm that you'd use in
practice), this is likely to perform poorly because the smallest
subtasks are too small to be worthwhile splitting up. Instead, as
is the case for nearly all fork/join applications, you'd pick some
minimum granularity size (for example 10 here) for which you always
sequentially solve rather than subdividing.
A recursive result-bearing {@link ForkJoinTask}.
<p>For a classic example, here is a task computing Fibonacci numbers:
<pre> {@code class Fibonacci extends RecursiveTask<Integer> { final int n; Fibonacci(int n) { this.n = n; } protected Integer compute() { if (n <= 1) return n; Fibonacci f1 = new Fibonacci(n - 1); f1.fork(); Fibonacci f2 = new Fibonacci(n - 2); return f2.compute() + f1.join(); } }}</pre>
However, besides being a dumb way to compute Fibonacci functions (there is a simple fast linear algorithm that you'd use in practice), this is likely to perform poorly because the smallest subtasks are too small to be worthwhile splitting up. Instead, as is the case for nearly all fork/join applications, you'd pick some minimum granularity size (for example 10 here) for which you always sequentially solve rather than subdividing.
@author Doug Lea