# criterion performance measurements

## overview

want to understand this report?

## fast minfree (whnf)/n=1

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

OLS regression | xxx | xxx | xxx |

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

Mean execution time | 5.3568058722169206e-8 | 5.560673534673787e-8 | 5.954391821909619e-8 |

Standard deviation | 5.615208465233548e-9 | 8.67033723401947e-9 | 1.382492056759818e-8 |

Outlying measurements have severe (0.9644423707437879%) effect on estimated standard deviation.

## fast minfree (whnf)/n=10

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

OLS regression | xxx | xxx | xxx |

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

Mean execution time | 4.3671030245992945e-7 | 4.723222498553126e-7 | 5.431431678858558e-7 |

Standard deviation | 1.0478209600033628e-7 | 1.689860711616648e-7 | 3.0596359441155127e-7 |

Outlying measurements have severe (0.9950617049244129%) effect on estimated standard deviation.

## fast minfree (whnf)/n=100

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

OLS regression | xxx | xxx | xxx |

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

Mean execution time | 4.2838062615429804e-6 | 4.466731365482713e-6 | 5.071784488903643e-6 |

Standard deviation | 2.6966776850244354e-7 | 1.035375818203981e-6 | 2.1189229751701073e-6 |

Outlying measurements have severe (0.9763641165108069%) effect on estimated standard deviation.

## fast minfree (whnf)/n=100

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

OLS regression | xxx | xxx | xxx |

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

Mean execution time | 4.7137026412056196e-5 | 5.1610591129288194e-5 | 5.974187906464943e-5 |

Standard deviation | 1.0728334754748877e-5 | 1.8737929671445553e-5 | 3.1772740591535295e-5 |

Outlying measurements have severe (0.9914098421831744%) effect on estimated standard deviation.

## fast minfree (whnf)/n=10000

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

OLS regression | xxx | xxx | xxx |

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

Mean execution time | 5.762525393162725e-4 | 6.756634424984028e-4 | 8.871594945158746e-4 |

Standard deviation | 3.12235319488742e-4 | 5.185155050795229e-4 | 8.264187593572102e-4 |

Outlying measurements have severe (0.9874891698541802%) effect on estimated standard deviation.

## slow minfree (whnf)/n=1

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

OLS regression | xxx | xxx | xxx |

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

Mean execution time | 7.632467067170168e-8 | 7.758677977554248e-8 | 7.925941581393601e-8 |

Standard deviation | 3.444606330316327e-9 | 4.939073022429046e-9 | 7.0173681166747964e-9 |

Outlying measurements have severe (0.8010093952294985%) effect on estimated standard deviation.

## slow minfree (whnf)/n=10

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

OLS regression | xxx | xxx | xxx |

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

Mean execution time | 1.0491801261467533e-6 | 1.0625807545065425e-6 | 1.087573636353871e-6 |

Standard deviation | 4.06485456440222e-8 | 5.4679560064573275e-8 | 7.292819566532802e-8 |

Outlying measurements have severe (0.6725025160367533%) effect on estimated standard deviation.

## slow minfree (whnf)/n=100

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

OLS regression | xxx | xxx | xxx |

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

Mean execution time | 5.271687015571286e-5 | 5.362573667512796e-5 | 5.5217018230114565e-5 |

Standard deviation | 2.8170893989263423e-6 | 3.858072117615767e-6 | 5.430692783629075e-6 |

Outlying measurements have severe (0.7183686210513552%) effect on estimated standard deviation.

## slow minfree (whnf)/n=100

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

OLS regression | xxx | xxx | xxx |

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

Mean execution time | 4.799027691638731e-3 | 4.846362072458015e-3 | 4.902091633450741e-3 |

Standard deviation | 1.211209335961744e-4 | 1.5608341583964034e-4 | 2.1871166720402718e-4 |

Outlying measurements have moderate (0.1384730141424549%) effect on estimated standard deviation.

## slow minfree (whnf)/n=10000

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

OLS regression | xxx | xxx | xxx |

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

Mean execution time | 0.5892713227476634 | 0.656838094916869 | 0.7792986824988475 |

Standard deviation | 7.223948494356591e-3 | 0.11748566507734186 | 0.14557605501897894 |

Outlying measurements have moderate (0.4694756131106758%) effect on estimated standard deviation.

## fast minfree (nf)/n=1

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

OLS regression | xxx | xxx | xxx |

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

Mean execution time | 6.473698237464403e-8 | 6.561264961026979e-8 | 6.693875233796099e-8 |

Standard deviation | 2.5990061248357448e-9 | 3.685241795873261e-9 | 5.489681955599441e-9 |

Outlying measurements have severe (0.7604931707675842%) effect on estimated standard deviation.

## fast minfree (nf)/n=10

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

OLS regression | xxx | xxx | xxx |

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

Mean execution time | 4.800017785023608e-7 | 4.951045440753281e-7 | 5.166292555428583e-7 |

Standard deviation | 4.716060908344581e-8 | 6.320776200757215e-8 | 9.007740108277621e-8 |

Outlying measurements have severe (0.9343204287921267%) effect on estimated standard deviation.

## fast minfree (nf)/n=100

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

OLS regression | xxx | xxx | xxx |

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

Mean execution time | 5.926174663773999e-6 | 6.568892441233159e-6 | 7.631980028997066e-6 |

Standard deviation | 1.5891733842508642e-6 | 2.5333009481294495e-6 | 3.596428840428825e-6 |

Outlying measurements have severe (0.9936560097932703%) effect on estimated standard deviation.

## fast minfree (nf)/n=100

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

OLS regression | xxx | xxx | xxx |

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

Mean execution time | 5.795761011929985e-5 | 6.354360493074316e-5 | 7.059378689077553e-5 |

Standard deviation | 1.4643724433872574e-5 | 2.0642897078989373e-5 | 2.6613583173707886e-5 |

Outlying measurements have severe (0.9829889529360802%) effect on estimated standard deviation.

## fast minfree (nf)/n=10000

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

OLS regression | xxx | xxx | xxx |

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

Mean execution time | 6.889953150264403e-4 | 7.746628554384198e-4 | 9.189369779946591e-4 |

Standard deviation | 2.261686805610041e-4 | 3.7226553335516834e-4 | 5.633143659599637e-4 |

Outlying measurements have severe (0.9859751002995701%) effect on estimated standard deviation.

## slow minfree (nf)/n=1

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

OLS regression | xxx | xxx | xxx |

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

Mean execution time | 9.500937738867239e-8 | 1.0673250682693119e-7 | 1.4431815550565986e-7 |

Standard deviation | 1.294395650176048e-8 | 6.155511955865685e-8 | 1.2220411463745473e-7 |

Outlying measurements have severe (0.995959015741357%) effect on estimated standard deviation.

## slow minfree (nf)/n=10

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

OLS regression | xxx | xxx | xxx |

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

Mean execution time | 1.415663173344334e-6 | 1.6515517149012235e-6 | 2.6801716665165776e-6 |

Standard deviation | 3.3098207228390095e-7 | 1.3022218219483123e-6 | 2.8628110876981195e-6 |

Outlying measurements have severe (0.994976045905243%) effect on estimated standard deviation.

## slow minfree (nf)/n=100

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

OLS regression | xxx | xxx | xxx |

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

Mean execution time | 6.048388245231568e-5 | 6.517251612525902e-5 | 7.33893748171163e-5 |

Standard deviation | 1.2778360548358491e-5 | 1.928675245060234e-5 | 2.6896273272086845e-5 |

Outlying measurements have severe (0.9824781785251278%) effect on estimated standard deviation.

## slow minfree (nf)/n=100

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

OLS regression | xxx | xxx | xxx |

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

Mean execution time | 5.709790266490142e-3 | 5.99034536394793e-3 | 6.452200882076918e-3 |

Standard deviation | 7.370533815637356e-4 | 1.1563492186345509e-3 | 1.8518040726016166e-3 |

Outlying measurements have severe (0.8636232221193819%) effect on estimated standard deviation.

## slow minfree (nf)/n=10000

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

OLS regression | xxx | xxx | xxx |

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

Mean execution time | 0.6264093771654491 | 0.6805699333951148 | 0.7347304896247806 |

Standard deviation | 4.6364809031611345e-2 | 6.492855084124166e-2 | 7.870553642089753e-2 |

Outlying measurements have moderate (0.22317658000700044%) 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.