• JMH:基准测试工具套件应用


    1.背景

    多线程性能测试

    JMH:简介

    JMH is a Java harness for building, running, and analysing nano/micro/milli/macro benchmarks written in Java and other languages targetting the JVM,

    由简介可知,JMH不止能对Java语言做基准测试,还能对运行在JVM上的其他语言做基准测试。而且可以分析到纳秒级别。

    2.生成maven工程

    推荐用法通过命令行创建,构建和运行JMH基准测试。

    执行如下命令:

    mvn archetype:generate -DinteractiveMode=false -DarchetypeGroupId=org.openjdk.jmh -DarchetypeArtifactId=jmh-java-benchmark-archetype -DgroupId=org.sample -DartifactId=test -Dversion=1.0

    特别提醒:命令不要换行

    3.启动工程编写测试代码

    将生成的代码使用idea打开:

    注意截图中已将代码拷贝到了其他地方

     编写一个1+2+3.....+10亿的测试代码

    比单线程与多线程的耗时

    /*
     * Copyright (c) 2005, 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.  Oracle designates this
     * particular file as subject to the "Classpath" exception as provided
     * by Oracle in the LICENSE file that accompanied this code.
     *
     * 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.sample;
    
    import org.openjdk.jmh.annotations.*;
    
    import java.util.concurrent.FutureTask;
    
    /**
     * 编写一个1+2+3.....+10亿的测试代码
     * <p>
     * 比单线程与多线程的耗时
     */
    @Fork(1)
    @BenchmarkMode(Mode.AverageTime)
    @Warmup(iterations = 3)
    @Measurement(iterations = 5)
    public class MyBenchmark {
        // 10亿
        static int num = 1000_000_000;
    
        /**
         * 多线程计算
         *
         * @return
         * @throws Exception
         */
        @Benchmark
        public int testMethodMore() throws Exception {
            FutureTask<Integer> t1 = new FutureTask<>(() -> {
                int sum = 0;
                for (int i = 0; i < 250_000_000; i++) {
                    sum += i;
                }
                return sum;
            });
            FutureTask<Integer> t2 = new FutureTask<>(() -> {
                int sum = 0;
                for (int i = 250_000_000; i < 500_000_000; i++) {
                    sum += i;
                }
                return sum;
            });
            FutureTask<Integer> t3 = new FutureTask<>(() -> {
                int sum = 0;
                for (int i = 500_000_000; i < 750_000_000; i++) {
                    sum += i;
                }
                return sum;
            });
            FutureTask<Integer> t4 = new FutureTask<>(() -> {
                int sum = 0;
                for (int i = 750_000_000; i < 1000_000_000; i++) {
                    sum += i;
                }
                return sum;
            });
            new Thread(t1).start();
            new Thread(t2).start();
            new Thread(t3).start();
            new Thread(t4).start();
            return t1.get() + t2.get() + t3.get() + t4.get();
        }
    
        /**
         * 普通循环计算
         *
         * @return
         * @throws Exception
         */
        @Benchmark
        public int testMethodSingle() throws Exception {
            FutureTask<Integer> t1 = new FutureTask<>(() -> {
                int sum = 0;
                for (int i = 0; i <= num; i++) {
                    sum += i;
                }
                return sum;
            });
            new Thread(t1).start();
            return t1.get();
        }
    }

    4.打包与运行

    打包:

     运行jar包:

     测试结果:

     可以明显看到多线程比单线程快

    提问:如果在单核CPU的情况下测试呢????? 

    这个问题很值钱,这设计到你对并发、并行、多线程等核心概念的理解

    测试完整日志如下:

    F:\JAVAEE高级课程体系\04_高级阶段\java多线程-高级\code\test>java -jar target\benchmarks.jar
    # VM invoker: D:\Program Files\Java\jre1.8.0_152\bin\java.exe
    # VM options: <none>
    # Warmup: 3 iterations, 1 s each
    # Measurement: 5 iterations, 1 s each
    # Threads: 1 thread, will synchronize iterations
    # Benchmark mode: Average time, time/op
    # Benchmark: org.sample.MyBenchmark.testMethodMore
    
    # Run progress: 0.00% complete, ETA 00:00:16
    # Fork: 1 of 1
    # Warmup Iteration   1: 0.130 s/op
    # Warmup Iteration   2: 0.120 s/op
    # Warmup Iteration   3: 0.115 s/op
    Iteration   1: 0.125 s/op
    Iteration   2: 0.118 s/op
    Iteration   3: 0.126 s/op
    Iteration   4: 0.114 s/op
    Iteration   5: 0.122 s/op
    
    
    Result: 0.121 ±(99.9%) 0.019 s/op [Average]
      Statistics: (min, avg, max) = (0.114, 0.121, 0.126), stdev = 0.005
      Confidence interval (99.9%): [0.102, 0.140]
    
    
    # VM invoker: D:\Program Files\Java\jre1.8.0_152\bin\java.exe
    # VM options: <none>
    # Warmup: 3 iterations, 1 s each
    # Measurement: 5 iterations, 1 s each
    # Threads: 1 thread, will synchronize iterations
    # Benchmark mode: Average time, time/op
    # Benchmark: org.sample.MyBenchmark.testMethodSingle
    
    # Run progress: 50.00% complete, ETA 00:00:10
    # Fork: 1 of 1
    # Warmup Iteration   1: 0.405 s/op
    # Warmup Iteration   2: 0.381 s/op
    # Warmup Iteration   3: 0.390 s/op
    Iteration   1: 0.400 s/op
    Iteration   2: 0.385 s/op
    Iteration   3: 0.378 s/op
    Iteration   4: 0.379 s/op
    Iteration   5: 0.388 s/op
    
    
    Result: 0.386 ±(99.9%) 0.033 s/op [Average]
      Statistics: (min, avg, max) = (0.378, 0.386, 0.400), stdev = 0.009
      Confidence interval (99.9%): [0.353, 0.419]
    
    
    # Run complete. Total time: 00:00:21
    
    Benchmark                           Mode  Samples  Score  Score error  Units
    o.s.MyBenchmark.testMethodMore      avgt        5  0.121        0.019   s/op
    o.s.MyBenchmark.testMethodSingle    avgt        5  0.386        0.033   s/op 

    该博客对应的视频教程

    https://www.cnblogs.com/newAndHui/p/15939255.html

    完美!

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  • 原文地址:https://www.cnblogs.com/newAndHui/p/15806740.html
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