# criterion performance measurements

## overview

want to understand this report?

## fib/1

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

OLS regression | xxx | xxx | xxx |

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

Mean execution time | 2.31459993168433e-8 | 2.374225969306158e-8 | 2.4336041431094957e-8 |

Standard deviation | 1.7147402747620926e-9 | 1.984234308811127e-9 | 2.3435359738948246e-9 |

Outlying measurements have severe (0.8827515417826841%) effect on estimated standard deviation.

## fib/5

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

OLS regression | xxx | xxx | xxx |

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

Mean execution time | 3.640686812141915e-7 | 3.7647973827317373e-7 | 3.8862828356384757e-7 |

Standard deviation | 3.5904833037515274e-8 | 4.150785932735141e-8 | 4.81505001531474e-8 |

Outlying measurements have severe (0.917699613099007%) effect on estimated standard deviation.

## fib/9

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

OLS regression | xxx | xxx | xxx |

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

Mean execution time | 2.5489390737084626e-6 | 2.614524699113428e-6 | 2.700766045605913e-6 |

Standard deviation | 2.0893167057513842e-7 | 2.4922772413717383e-7 | 3.0480780278156827e-7 |

Outlying measurements have severe (0.86814310186276%) effect on estimated standard deviation.

## fib/11

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

OLS regression | xxx | xxx | xxx |

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

Mean execution time | 6.347714383730146e-6 | 6.496202868182492e-6 | 6.668634037917654e-6 |

Standard deviation | 4.0420784296930194e-7 | 4.919233380857326e-7 | 6.202125623223447e-7 |

Outlying measurements have severe (0.7876656352417168%) 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.