javascript - benchmark js 的结果是什么意思？

Question

我正在为节点使用一个 Benchmark JS 版本，但我找不到任何关于如何读取结果的信息。

首先，是否有一个地方详细说明了您可以从 Benchmark JS 中提取的所有数据？

其次，我目前在我的控制台中得到以下结果:

Test x 2,276,094 ops/sec ±0.84% (190 runs sampled)

所有这些信息是什么意思？

Test:我的test的名字，我知道那个

x 2,276,094 ops/sec:我假设这是代码理论上可以在一秒钟内运行的平均次数？

±0.84%:不知道

190 runs sampled:基准运行代码以获得结果的次数？

Answer 1

您的问题可能不是重复的，但很有趣 the answer to it is作为GolezTroi在 a comment 中指出.

如果该问题被删除(极不可能)，这里是 John-David Dalton 的全文的回答:

I wrote Benchmark.js, which jsPerf uses.

1. "ops/sec" stands for operations per second.That is how many times a test is projected to execute in a second.

2. A test is repeatedly executed until it reaches the minimum time needed to get a percentage uncertainty for the measurement of less than or equal to 1%. The number of iterations will vary depending on the resolution of the environment’s timer and how many times a test can execute in the minimum run time. We collect completed test runs for 5 seconds (configurable), or at least 5 runs (also configurable), and then perform statistical analysis on the sample. So, a test may be repeated 100,000 times in 50 ms (the minimum run time for most environments), and then repeated 100 times more (5 seconds). A larger sample size (in this example, 100), leads to a smaller margin of error.

3. We base the decision of which test is faster on more than just ops/sec by also accounting for margin of error. For example, a test with a lower ops/sec but higher margin of error may be statistically indistinguishable from a test with higher ops/sec and lower margin of error.

   We used a welch t-test, similar to what SunSpider uses, but switched to an unpaired 2-sample t-test for equal variance (the variance is extremely small) because the welch t-test had problems comparing lower ops/sec and higher ops/sec with small variances which caused the degrees of freedom to be computed as less than 1. We also add a 5.5% allowance on tests with similar ops/sec because real world testing showed that identical tests can swing ~5% from test to re-test. T-tests are used to check that differences between tests are statistically significant.