Java基准测试的重要事项是:

Warm up the JIT first by running the code several times before timing it Make sure you run it for long enough to be able to measure the results in seconds or (better) tens of seconds While you can't call System.gc() between iterations, it's a good idea to run it between tests, so that each test will hopefully get a "clean" memory space to work with. (Yes, gc() is more of a hint than a guarantee, but it's very likely that it really will garbage collect in my experience.) I like to display iterations and time, and a score of time/iteration which can be scaled such that the "best" algorithm gets a score of 1.0 and others are scored in a relative fashion. This means you can run all algorithms for a longish time, varying both number of iterations and time, but still getting comparable results.

我正在写一篇关于。net基准测试框架设计的博客。我有一些以前的帖子，也许能给你一些想法——当然，不是每件事都合适，但其中一些可能是合适的。

Java基准测试的重要事项是:

Warm up the JIT first by running the code several times before timing it Make sure you run it for long enough to be able to measure the results in seconds or (better) tens of seconds While you can't call System.gc() between iterations, it's a good idea to run it between tests, so that each test will hopefully get a "clean" memory space to work with. (Yes, gc() is more of a hint than a guarantee, but it's very likely that it really will garbage collect in my experience.) I like to display iterations and time, and a score of time/iteration which can be scaled such that the "best" algorithm gets a score of 1.0 and others are scored in a relative fashion. This means you can run all algorithms for a longish time, varying both number of iterations and time, but still getting comparable results.

我正在写一篇关于。net基准测试框架设计的博客。我有一些以前的帖子，也许能给你一些想法——当然，不是每件事都合适，但其中一些可能是合适的。

在Java中编写微基准测试有许多可能的陷阱。

首先:你必须计算各种随机占用时间的事件:垃圾收集、缓存效果(OS对文件的缓存效果和CPU对内存的缓存效果)、IO等。

第二:对于非常短的时间间隔，您不能相信测量时间的准确性。

第三:JVM在执行时优化代码。因此，在同一个jvm实例中不同的运行将变得越来越快。

My recommendations: Make your benchmark run some seconds, that is more reliable than a runtime over milliseconds. Warm up the JVM (means running the benchmark at least once without measuring, that the JVM can run optimizations). And run your benchmark multiple times (maybe 5 times) and take the median-value. Run every micro-benchmark in a new JVM-instance (call for every benchmark new Java) otherwise optimization effects of the JVM can influence later running tests. Don't execute things, that aren't executed in the warmup-phase (as this could trigger class-load and recompilation).

如果您正在尝试比较两种算法，那么每种算法至少要进行两次基准测试，交替使用顺序。例如:

for(i=1..n)
  alg1();
for(i=1..n)
  alg2();
for(i=1..n)
  alg2();
for(i=1..n)
  alg1();

我发现了一些明显的差异(有时5-10%)在运行时相同的算法在不同的通行证。

此外，还要确保n非常大，以便每个循环的运行时间至少为10秒左右。迭代次数越多，基准测试时间中的数字就越重要，数据就越可靠。

还应该注意的是，在比较不同的实现时，分析微基准测试的结果可能也很重要。因此，应进行显著性检验。

这是因为在基准测试的大多数运行过程中，实现A可能比实现B更快，但A也可能有更高的差异，因此与B相比，A的测量性能收益将没有任何意义。

因此正确编写和运行微基准测试也很重要，但正确分析它也很重要。

确保以某种方式使用在基准代码中计算的结果。否则你的代码会被优化掉。