criterion performance measurements


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lower bound estimate upper bound
OLS regression xxx xxx xxx
R² goodness-of-fit xxx xxx xxx
Mean execution time {{anMean.estLowerBound}} {{anMean.estPoint}} {{anMean.estUpperBound}}
Standard deviation {{anStdDev.estLowerBound}} {{anStdDev.estPoint}} {{anStdDev.estUpperBound}}

Outlying measurements have {{anOutlierVar.ovDesc}} ({{anOutlierVar.ovFraction}}%) 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.