/* * Copyright (c) 2014, Oracle and/or its affiliates. All rights reserved. * DO NOT ALTER OR REMOVE COPYRIGHT NOTICES OR THIS FILE HEADER. * * This code is free software; you can redistribute it and/or modify it * under the terms of the GNU General Public License version 2 only, as * published by the Free Software Foundation. * * This code is distributed in the hope that it will be useful, but WITHOUT * ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or * FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License * version 2 for more details (a copy is included in the LICENSE file that * accompanied this code). * * You should have received a copy of the GNU General Public License version * 2 along with this work; if not, write to the Free Software Foundation, * Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA. * * Please contact Oracle, 500 Oracle Parkway, Redwood Shores, CA 94065 USA * or visit www.oracle.com if you need additional information or have any * questions. */ package org.openjdk.bench.java.util.concurrent; import org.openjdk.jmh.annotations.Benchmark; import org.openjdk.jmh.annotations.OutputTimeUnit; import org.openjdk.jmh.annotations.Param; import org.openjdk.jmh.annotations.Scope; import org.openjdk.jmh.annotations.Setup; import org.openjdk.jmh.annotations.State; import org.openjdk.jmh.annotations.TearDown; import java.util.concurrent.ExecutionException; import java.util.concurrent.ForkJoinPool; import java.util.concurrent.ForkJoinTask; import java.util.concurrent.RecursiveTask; import java.util.concurrent.TimeUnit; /** * Benchmark assesses ForkJoinPool performance with dependence on threshold. */ @OutputTimeUnit(TimeUnit.MINUTES) @State(Scope.Benchmark) public class ForkJoinPoolThresholdAutoQueued { /** * Implementation notes: * * This test solves the problem with threshold = 1, and adaptive heuristics. The optimal level is static, * and lies somewhere in 1..2 interval. Note the test degrades significantly when heuristic starts to fail, * and the throughput is buried under FJP overheads. * * Baseline includes solving problem sequentially. Hence, each test provides the speedup for parallel execution * versus sequential version. */ @Param("0") private int workers; @Param("10000000") private int size; @Param({"1", "2", "3", "4", "5", "6", "7", "8"}) private int threshold; private ForkJoinPool fjp; private Problem problem; @Setup public void setup() { if (workers == 0) { workers = Runtime.getRuntime().availableProcessors(); } problem = new Problem(size); fjp = new ForkJoinPool(workers); } @TearDown public void teardown() { fjp.shutdownNow(); } @Benchmark public long baselineRaw() { return problem.solve(); } @Benchmark public Long test() throws ExecutionException, InterruptedException { return fjp.invoke(new AutoQueuedTask(threshold, problem, 0, problem.size())); } private static class AutoQueuedTask extends RecursiveTask { private final int thr; private final Problem problem; private final int l; private final int r; public AutoQueuedTask(int thr, Problem p, int l, int r) { this.thr = thr; this.problem = p; this.l = l; this.r = r; } @Override protected Long compute() { if (r - l <= 1 || getQueuedTaskCount() >= thr) { return problem.solve(l, r); } int mid = (l + r) >>> 1; ForkJoinTask t1 = new AutoQueuedTask(thr, problem, l, mid); ForkJoinTask t2 = new AutoQueuedTask(thr, problem, mid, r); t2.fork(); long res = 0; res += t1.invoke(); res += t2.join(); return res; } } }