java - Java for循环性能问题

考虑这个例子：

public static void main(final String[] args) {
    final List<String> myList = Arrays.asList("A", "B", "C", "D");
    final long start = System.currentTimeMillis();
    for (int i = 1000000; i > myList.size(); i--) {
        System.out.println("Hello");
    }
    final long stop = System.currentTimeMillis();
    System.out.println("Finish: " + (stop - start));
}

与

public static void main(final String[] args) {
    final List<String> myList = Arrays.asList("A", "B", "C", "D");
    final long start = System.currentTimeMillis();
    final int size = myList.size();
    for (int i = 1000000; i > size; i--) {
        System.out.println("Hello");
    }
    final long stop = System.currentTimeMillis();
    System.out.println("Finish: " + (stop - start));
}

这会有所区别吗？在我的机器上，第二台机器似乎执行得更快，但我不知道它是否真的准确。编译器会优化此代码吗？我可以认为如果循环条件是不可变的对象（例如String数组），他会这样做。

最佳答案

如果您想测试类似的东西，您确实必须优化您的微基准测试以衡量您关心的内容。

首先，使循环便宜，但无法跳过。通常计算总和就可以了。

其次，比较两个时间。

这是同时执行以下操作的一些代码：

import java.util.*;

public class Test {

public static long run1() {
  final List<String> myList = Arrays.asList("A", "B", "C", "D");
  final long start = System.nanoTime();
  int sum = 0;
  for (int i = 1000000000; i > myList.size(); i--) sum += i;
  final long stop = System.nanoTime();
  System.out.println("Finish: " + (stop - start)*1e-9 + " ns/op; sum = " + sum);
  return stop-start;
}

public static long run2() {
  final List<String> myList = Arrays.asList("A", "B", "C", "D");
  final long start = System.nanoTime();
  int sum = 0;
  int limit = myList.size();
  for (int i = 1000000000; i > limit; i--) sum += i;
  final long stop = System.nanoTime();
  System.out.println("Finish: " + (stop - start)*1e-9 + " ns/op; sum = " + sum);
  return stop-start;
}

public static void main(String[] args) {
  for (int i=0 ; i<5 ; i++) {
    long t1 = run1();
    long t2 = run2();
    System.out.println("  Speedup = " + (t1-t2)*1e-9 + " ns/op\n");
  }
}

}

如果我们运行它，在我的系统上，我们将得到：

Finish: 0.481741256 ns/op; sum = -243309322
Finish: 0.40228402 ns/op; sum = -243309322
  Speedup = 0.079457236 ns/op

Finish: 0.450627151 ns/op; sum = -243309322
Finish: 0.43534661700000005 ns/op; sum = -243309322
  Speedup = 0.015280534 ns/op

Finish: 0.47738474700000005 ns/op; sum = -243309322
Finish: 0.403698331 ns/op; sum = -243309322
  Speedup = 0.073686416 ns/op

Finish: 0.47729349600000004 ns/op; sum = -243309322
Finish: 0.405540508 ns/op; sum = -243309322
  Speedup = 0.071752988 ns/op

Finish: 0.478979617 ns/op; sum = -243309322
Finish: 0.36067492700000003 ns/op; sum = -243309322
  Speedup = 0.11830469 ns/op

这意味着方法调用的开销约为0.1 ns。如果您的循环执行不超过1-2 ns的操作，那么您应该注意这一点。否则，请不要。