# criterion performance measurements

## overview

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## readPng/Friday

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

OLS regression | xxx | xxx | xxx |

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

Mean execution time | 4.0296437433992645e-2 | 4.160194762801689e-2 | 4.29852486444528e-2 |

Standard deviation | 2.2965550641239943e-3 | 2.726123453272308e-3 | 3.3898650437080733e-3 |

Outlying measurements have moderate (0.19029657516669457%) effect on estimated standard deviation.

## readPng/Yarr

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

OLS regression | xxx | xxx | xxx |

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

Mean execution time | 1.4287699722327902e-2 | 1.4638736620285852e-2 | 1.5049276857336147e-2 |

Standard deviation | 8.620171546433233e-4 | 9.849613064321502e-4 | 1.084357558115183e-3 |

Outlying measurements have moderate (0.3058632743959752%) effect on estimated standard deviation.

## readPng/Repa

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

OLS regression | xxx | xxx | xxx |

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

Mean execution time | 1.3950702573715786e-2 | 1.4289071185874193e-2 | 1.5094085640199175e-2 |

Standard deviation | 6.475623045023674e-4 | 1.1689538950930979e-3 | 1.7626160118983322e-3 |

Outlying measurements have moderate (0.40700386228795377%) effect on estimated standard deviation.

## readPng/OpenCV

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

OLS regression | xxx | xxx | xxx |

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

Mean execution time | 1.3541878007784143e-2 | 1.3558980859203686e-2 | 1.357151852865896e-2 |

Standard deviation | 2.5669962689635966e-5 | 3.9108421709150414e-5 | 5.126934719823398e-5 |

Outlying measurements have slight (3.698224852071006e-2%) effect on estimated standard deviation.

## threshold/Friday

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

OLS regression | xxx | xxx | xxx |

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

Mean execution time | 8.412049958298091e-2 | 8.468482119912818e-2 | 8.568243254296161e-2 |

Standard deviation | 3.427505145012401e-4 | 1.1761388867812818e-3 | 1.578378078968777e-3 |

Outlying measurements have slight (9.0e-2%) effect on estimated standard deviation.

## threshold/UnmHip

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

OLS regression | xxx | xxx | xxx |

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

Mean execution time | 0.7190512528179074 | 0.7510140796761462 | 0.8085057602756741 |

Standard deviation | 0.0 | 5.0715983932832005e-2 | 5.536124007199667e-2 |

Outlying measurements have moderate (0.19646392854406713%) effect on estimated standard deviation.

## threshold/Yarr

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

OLS regression | xxx | xxx | xxx |

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

Mean execution time | 2.087439687914669e-2 | 2.2333869700835104e-2 | 2.3230383307830272e-2 |

Standard deviation | 1.929600813066298e-3 | 2.88591053415312e-3 | 4.441759623266922e-3 |

Outlying measurements have severe (0.5831556304694465%) effect on estimated standard deviation.

## threshold/Repa

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

OLS regression | xxx | xxx | xxx |

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

Mean execution time | 8.046052467121084e-2 | 8.330857363296462e-2 | 8.639004037062764e-2 |

Standard deviation | 3.2324049496944575e-3 | 4.707040103975081e-3 | 6.441254915684316e-3 |

Outlying measurements have moderate (0.18042221215982251%) effect on estimated standard deviation.

## threshold/OpenCV

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

OLS regression | xxx | xxx | xxx |

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

Mean execution time | 2.8072532821077166e-4 | 2.902969378342568e-4 | 3.0185400118765924e-4 |

Standard deviation | 3.352328579390689e-5 | 3.970874824705555e-5 | 4.895417958393635e-5 |

Outlying measurements have severe (0.8785971013380458%) effect on estimated standard deviation.

## mean/Friday

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

OLS regression | xxx | xxx | xxx |

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

Mean execution time | 2.4422359205509183 | 2.4826367387309123 | 2.5483196662744185 |

Standard deviation | 0.0 | 5.7383177561751667e-2 | 6.124747915010666e-2 |

Outlying measurements have moderate (0.1875%) effect on estimated standard deviation.

## mean/UnmHip

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

OLS regression | xxx | xxx | xxx |

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

Mean execution time | 0.5876445450503142 | 0.592728506092269 | 0.5954234559757089 |

Standard deviation | 0.0 | 4.426690811402495e-3 | 4.667790121969822e-3 |

Outlying measurements have moderate (0.1875%) effect on estimated standard deviation.

## mean/Yarr

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

OLS regression | xxx | xxx | xxx |

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

Mean execution time | 8.045367425418132e-2 | 8.540176848276139e-2 | 9.554343099856842e-2 |

Standard deviation | 4.2952585916042835e-3 | 1.1427345104186845e-2 | 1.7820638160914455e-2 |

Outlying measurements have moderate (0.4781825958633418%) effect on estimated standard deviation.

## mean/Repa

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

OLS regression | xxx | xxx | xxx |

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

Mean execution time | 0.1267233467753103 | 0.16039022776293274 | 0.25271230010232737 |

Standard deviation | 5.775822039239866e-3 | 7.353299042553409e-2 | 0.10121668692332929 |

Outlying measurements have severe (0.8493406089966716%) effect on estimated standard deviation.

## mean/OpenCV

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

OLS regression | xxx | xxx | xxx |

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

Mean execution time | 2.8918431882824663e-3 | 2.9209452379390342e-3 | 2.9461597450619334e-3 |

Standard deviation | 7.511795160383317e-5 | 9.346565496654255e-5 | 1.2654026225123033e-4 |

Outlying measurements have moderate (0.16715227641973768%) 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.