criterion performance measurements

overview

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reverse/library reverse

lower bound estimate upper bound
OLS regression xxx xxx xxx
R² goodness-of-fit xxx xxx xxx
Mean execution time 9.716982987248993e-6 9.9180780297361e-6 1.0447278183988108e-5
Standard deviation 1.3388781614736357e-7 9.598205750089096e-7 1.8853978503660426e-6

Outlying measurements have severe (0.8522363781116609%) effect on estimated standard deviation.

reverse/simple reverse

lower bound estimate upper bound
OLS regression xxx xxx xxx
R² goodness-of-fit xxx xxx xxx
Mean execution time 5.911826185641671e-3 6.047827167612605e-3 6.321378939453348e-3
Standard deviation 3.1389398214789555e-4 5.20801130812429e-4 7.509912061706823e-4

Outlying measurements have severe (0.5109560399504653%) effect on estimated standard deviation.

reverse/fast reverse

lower bound estimate upper bound
OLS regression xxx xxx xxx
R² goodness-of-fit xxx xxx xxx
Mean execution time 9.376768723899086e-6 9.49132970987831e-6 9.731205130877708e-6
Standard deviation 3.3333059152460446e-7 5.502590589383045e-7 8.651601660204759e-7

Outlying measurements have severe (0.6774598549149854%) effect on estimated standard deviation.

understanding this report

In this report, each function benchmarked by criterion is assigned a section of its own. The charts in each section are active; if you hover your mouse over data points and annotations, you will see more details.

Under the charts is a small table. The first two rows are the results of a linear regression run on the measurements displayed in the right-hand chart.

We use a statistical technique called the bootstrap to provide confidence intervals on our estimates. The bootstrap-derived upper and lower bounds on estimates let you see how accurate we believe those estimates to be. (Hover the mouse over the table headers to see the confidence levels.)

A noisy benchmarking environment can cause some or many measurements to fall far from the mean. These outlying measurements can have a significant inflationary effect on the estimate of the standard deviation. We calculate and display an estimate of the extent to which the standard deviation has been inflated by outliers.