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/*
* Written by Doug Lea with assistance from members of JCP JSR-166
* Expert Group and released to the public domain, as explained at
* http://creativecommons.org/publicdomain/zero/1.0/
*/
package scala.concurrent.forkjoin;
/**
* 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; }
* 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.
*
* @since 1.7
* @author Doug Lea
*/
public abstract class RecursiveTask<V> extends ForkJoinTask<V> {
private static final long serialVersionUID = 5232453952276485270L;
/**
* The result of the computation.
*/
V result;
/**
* The main computation performed by this task.
*/
protected abstract V compute();
public final V getRawResult() {
return result;
}
protected final void setRawResult(V value) {
result = value;
}
/**
* Implements execution conventions for RecursiveTask.
*/
protected final boolean exec() {
result = compute();
return true;
}
}
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