# criterion performance measurements

## overview

want to understand this report?

## vector, pure

lower bound | estimate | upper bound | |
---|---|---|---|

OLS regression | xxx | xxx | xxx |

R² goodness-of-fit | xxx | xxx | xxx |

Mean execution time | 5.7774416513684904e-5 | 5.786299529054173e-5 | 5.800763557211316e-5 |

Standard deviation | 2.8450897785973335e-7 | 3.979815779558528e-7 | 5.535760263512166e-7 |

Outlying measurements have no (7.751464843750192e-3%) effect on estimated standard deviation.

## vector, monadic

lower bound | estimate | upper bound | |
---|---|---|---|

OLS regression | xxx | xxx | xxx |

R² goodness-of-fit | xxx | xxx | xxx |

Mean execution time | 3.0445285073367397e-4 | 3.047244100465291e-4 | 3.053036702422851e-4 |

Standard deviation | 7.592917484590238e-7 | 1.3017537856182755e-6 | 2.3620772058639825e-6 |

Outlying measurements have slight (1.0525124490719783e-2%) effect on estimated standard deviation.

## conduit, pure

lower bound | estimate | upper bound | |
---|---|---|---|

OLS regression | xxx | xxx | xxx |

R² goodness-of-fit | xxx | xxx | xxx |

Mean execution time | 5.805609321175472e-5 | 5.8224971073199394e-5 | 5.8502686143135456e-5 |

Standard deviation | 5.102876940446677e-7 | 6.819033115391721e-7 | 1.0123197262726193e-6 |

Outlying measurements have slight (5.902349342649713e-2%) effect on estimated standard deviation.

## conduit, monadic

lower bound | estimate | upper bound | |
---|---|---|---|

OLS regression | xxx | xxx | xxx |

R² goodness-of-fit | xxx | xxx | xxx |

Mean execution time | 8.619200844734316e-5 | 8.627590696536751e-5 | 8.637983278871134e-5 |

Standard deviation | 2.4294026820002905e-7 | 2.9620449605356533e-7 | 3.9233199238636454e-7 |

Outlying measurements have no (8.263888888888888e-3%) 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.

- The chart on the left is a kernel density estimate (also known as a KDE) of time measurements. This graphs the probability of any given time measurement occurring. A spike indicates that a measurement of a particular time occurred; its height indicates how often that measurement was repeated.
- The chart on the right is the raw data from which the kernel
density estimate is built. The
*x*axis indicates the number of loop iterations, while the*y*axis shows measured execution time for the given number of loop iterations. The line behind the values is the linear regression prediction of execution time for a given number of iterations. Ideally, all measurements will be on (or very near) this line.

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.

*OLS regression*indicates the time estimated for a single loop iteration using an ordinary least-squares regression model. This number is more accurate than the*mean*estimate below it, as it more effectively eliminates measurement overhead and other constant factors.*R² goodness-of-fit*is a measure of how accurately the linear regression model fits the observed measurements. If the measurements are not too noisy, R² should lie between 0.99 and 1, indicating an excellent fit. If the number is below 0.99, something is confounding the accuracy of the linear model.*Mean execution time*and*standard deviation*are statistics calculated from execution time divided by number of iterations.

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.