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Java中對AtomicInteger和int值在多線程下遞增操作的測試

更新時間：2014年09月19日 09:56:12 投稿：junjie

這篇文章主要介紹了Java中對AtomicInteger和int值在多線程下遞增操作的測試,本文得出AtomicInteger操作與 int操作的效率大致相差在50-80倍上下的結(jié)論,需要的朋友可以參考下

Java針對多線程下的數(shù)值安全計數(shù)器設(shè)計了一些類,這些類叫做原子類,其中一部分如下:

java.util.concurrent.atomic.AtomicBoolean;
java.util.concurrent.atomic.AtomicInteger;
java.util.concurrent.atomic.AtomicLong;
java.util.concurrent.atomic.AtomicReference;

下面是一個對比 AtomicInteger 與普通 int 值在多線程下的遞增測試,使用的是 junit4;

完整代碼:

package test.java;

import java.util.concurrent.CountDownLatch;
import java.util.concurrent.atomic.AtomicInteger;

import org.junit.Assert;
import org.junit.Before;
import org.junit.Test;

/**
 * 測試AtomicInteger與普通int值在多線程下的遞增操作
 */
public class TestAtomic {

 // 原子Integer遞增對象
 public static AtomicInteger counter_integer;// = new AtomicInteger(0);
 // 一個int類型的變量
 public static int count_int = 0;

 @Before
 public void setUp() {
 // 所有測試開始之前執(zhí)行初始設(shè)置工作
 counter_integer = new AtomicInteger(0);
 }

 @Test
 public void testAtomic() throws InterruptedException {
 // 創(chuàng)建的線程數(shù)量
 int threadCount = 100;
 // 其他附屬線程內(nèi)部循環(huán)多少次
 int loopCount = 10000600;
 // 控制附屬線程的輔助對象;(其他await的線程先等著主線程喊開始)
 CountDownLatch latch_1 = new CountDownLatch(1);
 // 控制主線程的輔助對象;(主線程等著所有附屬線程都運行完畢再繼續(xù))
 CountDownLatch latch_n = new CountDownLatch(threadCount);
 // 創(chuàng)建并啟動其他附屬線程
 for (int i = 0; i < threadCount; i++) {
  Thread thread = new AtomicIntegerThread(latch_1, latch_n, loopCount);
  thread.start();
 }
 long startNano = System.nanoTime();
 // 讓其他等待的線程統(tǒng)一開始
 latch_1.countDown();
 // 等待其他線程執(zhí)行完
 latch_n.await();
 //

 long endNano = System.nanoTime();
 int sum = counter_integer.get();
 //
 Assert.assertEquals("sum 不等于 threadCount * loopCount,測試失敗",
  sum, threadCount * loopCount);
 System.out.println("--------testAtomic(); 預(yù)期兩者相等------------");
 System.out.println("耗時: " + ((endNano - startNano) / (1000 * 1000)) + "ms");
 System.out.println("threadCount = " + (threadCount) + ";");
 System.out.println("loopCount = " + (loopCount) + ";");
 System.out.println("sum = " + (sum) + ";");
 }

 @Test
 public void testIntAdd() throws InterruptedException {
 // 創(chuàng)建的線程數(shù)量
 int threadCount = 100;
 // 其他附屬線程內(nèi)部循環(huán)多少次
 int loopCount = 10000600;
 // 控制附屬線程的輔助對象;(其他await的線程先等著主線程喊開始)
 CountDownLatch latch_1 = new CountDownLatch(1);
 // 控制主線程的輔助對象;(主線程等著所有附屬線程都運行完畢再繼續(xù))
 CountDownLatch latch_n = new CountDownLatch(threadCount);
 // 創(chuàng)建并啟動其他附屬線程
 for (int i = 0; i < threadCount; i++) {
  Thread thread = new IntegerThread(latch_1, latch_n, loopCount);
  thread.start();
 }
 long startNano = System.nanoTime();
 // 讓其他等待的線程統(tǒng)一開始
 latch_1.countDown();
 // 等待其他線程執(zhí)行完
 latch_n.await();
 //
 long endNano = System.nanoTime();
 int sum = count_int;
 //
 Assert.assertNotEquals(
  "sum 等于 threadCount * loopCount,testIntAdd()測試失敗", 
  sum, threadCount * loopCount);
 System.out.println("-------testIntAdd(); 預(yù)期兩者不相等---------");
 System.out.println("耗時: " + ((endNano - startNano) / (1000*1000))+ "ms");
 System.out.println("threadCount = " + (threadCount) + ";");
 System.out.println("loopCount = " + (loopCount) + ";");
 System.out.println("sum = " + (sum) + ";");
 }

 // 線程
 class AtomicIntegerThread extends Thread {
 private CountDownLatch latch = null;
 private CountDownLatch latchdown = null;
 private int loopCount;

 public AtomicIntegerThread(CountDownLatch latch,
  CountDownLatch latchdown, int loopCount) {
  this.latch = latch;
  this.latchdown = latchdown;
  this.loopCount = loopCount;
 }

 @Override
 public void run() {
  // 等待信號同步
  try {
  this.latch.await();
  } catch (InterruptedException e) {
  e.printStackTrace();
  }
  //
  for (int i = 0; i < loopCount; i++) {
  counter_integer.getAndIncrement();
  }
  // 通知遞減1次
  latchdown.countDown();
 }
 }

 // 線程
 class IntegerThread extends Thread {
 private CountDownLatch latch = null;
 private CountDownLatch latchdown = null;
 private int loopCount;

 public IntegerThread(CountDownLatch latch, 
  CountDownLatch latchdown, int loopCount) {
  this.latch = latch;
  this.latchdown = latchdown;
  this.loopCount = loopCount;
 }

 @Override
 public void run() {
  // 等待信號同步
  try {
  this.latch.await();
  } catch (InterruptedException e) {
  e.printStackTrace();
  }
  //
  for (int i = 0; i < loopCount; i++) {
  count_int++;
  }
  // 通知遞減1次
  latchdown.countDown();
 }
 }
}

普通PC機上的執(zhí)行結(jié)果類似如下:

--------------testAtomic(); 預(yù)期兩者相等-------------------
耗時: 85366ms
threadCount = 100;
loopCount = 10000600;
sum = 1000060000;
--------------testIntAdd(); 預(yù)期兩者不相等-------------------
耗時: 1406ms
threadCount = 100;
loopCount = 10000600;
sum = 119428988;

從中可以看出, AtomicInteger操作與 int操作的效率大致相差在50-80倍上下,當(dāng)然,int很不消耗時間,這個對比只是提供一個參照。

如果確定是單線程執(zhí)行,那應(yīng)該使用 int; 而int在多線程下的操作執(zhí)行的效率還是蠻高的, 10億次只花了1.5秒鐘；

(假設(shè)CPU是 2GHZ，雙核4線程,理論最大8GHZ，則每秒理論上有80億個時鐘周期,

10億次Java的int增加消耗了1.5秒,即 120億次運算, 算下來每次循環(huán)消耗CPU周期 12個;

個人覺得效率不錯, C 語言也應(yīng)該需要4個以上的時鐘周期(判斷,執(zhí)行內(nèi)部代碼,自增判斷,跳轉(zhuǎn))

前提是: JVM和CPU沒有進行激進優(yōu)化.

)

而 AtomicInteger 效率其實也不低,10億次消耗了80秒, 那100萬次大約也就是千分之一,80毫秒的樣子.

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