/* * 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/licenses/publicdomain */ package jsr166y; /** * Recursive result-bearing ForkJoinTasks. * <p> For a classic example, here is a task computing Fibonacci numbers: * * <pre> * class Fibonacci extends RecursiveTask<Integer> { * final int n; * Fibonnaci(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. * */ public abstract class RecursiveTask<V> extends ForkJoinTask<V> { /** * Empty contructor for use by subclasses. */ protected RecursiveTask() { } /** * The result returned by compute method. */ 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; } }