# criterion performance measurements

## overview

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## heapsort/5

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

OLS regression | xxx | xxx | xxx |

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

Mean execution time | 1.58329799273574e-7 | 1.598936880522252e-7 | 1.6242863491503614e-7 |

Standard deviation | 4.489394070695717e-9 | 7.0433189156298065e-9 | 9.999742433328496e-9 |

Outlying measurements have severe (0.6407603280788239%) effect on estimated standard deviation.

## heapsort/50

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

OLS regression | xxx | xxx | xxx |

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

Mean execution time | 1.8065049448569014e-6 | 1.8246429561268866e-6 | 1.843178550971223e-6 |

Standard deviation | 5.271409072874056e-8 | 6.006083572501827e-8 | 8.1531077461685e-8 |

Outlying measurements have moderate (0.4421328038962608%) effect on estimated standard deviation.

## heapsort/100

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

OLS regression | xxx | xxx | xxx |

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

Mean execution time | 3.8634966872522e-6 | 3.875120991581294e-6 | 3.886219739892984e-6 |

Standard deviation | 2.8720730152430398e-8 | 3.751658281181747e-8 | 5.464232086271259e-8 |

Outlying measurements have slight (6.1363911963249175e-2%) effect on estimated standard deviation.

## heapsort/500

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

OLS regression | xxx | xxx | xxx |

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

Mean execution time | 1.779436206388321e-6 | 1.8094681214222466e-6 | 1.8659466682482388e-6 |

Standard deviation | 6.604813940934906e-8 | 1.3394151158057795e-7 | 2.155282704710146e-7 |

Outlying measurements have severe (0.8045738794371321%) 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.