benchmark js 的结果是什么意思？
What do the results from benchmark js mean?
我正在使用 Node 的 Benchmark JS 版本，但找不到有关如何读取结果的任何信息。
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？
测试:我测试的名字，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 运行s sampled: 多少次benchmark运行代码得到结果?
您的问题可能不是重复的，但有趣的是 the answer to it is as GolezTroi points out in 。
如果该问题被删除（极不可能），这里是 John-David Dalton 的答案全文：
I wrote Benchmark.js, which jsPerf uses.
"ops/sec" stands for operations per second.
That is how many times a test is projected to execute in a second.
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.
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.
我正在使用 Node 的 Benchmark JS 版本，但找不到有关如何读取结果的任何信息。
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？
测试:我测试的名字，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 运行s sampled: 多少次benchmark运行代码得到结果?
您的问题可能不是重复的，但有趣的是 the answer to it is as GolezTroi points out in
如果该问题被删除（极不可能），这里是 John-David Dalton 的答案全文：
I wrote Benchmark.js, which jsPerf uses.
"
ops/sec" stands for operations per second. That is how many times a test is projected to execute in a second.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 for5seconds (configurable), or at least5runs (also configurable), and then perform statistical analysis on the sample. So, a test may be repeated100,000times in50 ms(the minimum run time for most environments), and then repeated100times more (5seconds). A larger sample size (in this example,100), leads to a smaller margin of error.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 a5.5%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.