criterion performance measurements
overview
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Small/massiv
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 8.26827736749882e-3 | 8.547466070735064e-3 | 8.989757082912631e-3 |
| Standard deviation | 5.927407727827519e-4 | 8.736453790303661e-4 | 1.281037094915477e-3 |
Outlying measurements have severe (0.5772728651599865%) effect on estimated standard deviation.
Small/repa
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 3.130644130598954e-3 | 3.5200493278457596e-3 | 4.696450230112041e-3 |
| Standard deviation | 1.0251544169617819e-4 | 2.010498530229798e-3 | 4.04547031302125e-3 |
Outlying measurements have severe (0.9786538337080675%) effect on estimated standard deviation.
Small/accelerate
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 1.95309482476037e-2 | 2.0259087726450024e-2 | 2.201384719402562e-2 |
| Standard deviation | 1.2956450398474407e-3 | 2.501255836973593e-3 | 4.274535898717403e-3 |
Outlying measurements have severe (0.5554900447490461%) effect on estimated standard deviation.
Small/yarr
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 5.134423752681615e-3 | 5.358765856307984e-3 | 5.611330030057861e-3 |
| Standard deviation | 5.22613376219076e-4 | 7.111953369592244e-4 | 9.671757231046544e-4 |
Outlying measurements have severe (0.7375251789712716%) effect on estimated standard deviation.
Small/friday
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 2.9791309564080472e-2 | 3.0010808235573998e-2 | 3.0271438267951114e-2 |
| Standard deviation | 3.746036687231643e-4 | 5.089748689882566e-4 | 7.106392280907312e-4 |
Outlying measurements have slight (5.536332179930785e-2%) effect on estimated standard deviation.
Medium/massiv
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 3.137955460876159e-2 | 3.2641270050566634e-2 | 3.469626001164507e-2 |
| Standard deviation | 1.6062085720952499e-3 | 3.519390400880053e-3 | 5.676776053436707e-3 |
Outlying measurements have moderate (0.4530795941078996%) effect on estimated standard deviation.
Medium/repa
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 6.0935277328633304e-2 | 6.183649764841473e-2 | 6.518668861197768e-2 |
| Standard deviation | 8.406669693726138e-4 | 2.5705640472410314e-3 | 4.622110669043423e-3 |
Outlying measurements have slight (7.810083381497201e-2%) effect on estimated standard deviation.
Medium/accelerate
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 7.059560840560357e-2 | 7.138457971479462e-2 | 7.329865751699323e-2 |
| Standard deviation | 7.25138451823791e-4 | 2.1170257143967738e-3 | 3.6096066997452175e-3 |
Outlying measurements have slight (8.264462809917356e-2%) effect on estimated standard deviation.
Medium/yarr
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 8.734587477520109e-2 | 8.998451891743267e-2 | 9.332958341576159e-2 |
| Standard deviation | 3.4793145113671967e-3 | 5.05597353175047e-3 | 7.412707307426668e-3 |
Outlying measurements have moderate (0.18020269690409577%) effect on estimated standard deviation.
Medium/friday
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 1.001776173361577 | 1.0098564689202856 | 1.0146538227563724 |
| Standard deviation | 3.840586092943965e-3 | 8.085732665103462e-3 | 1.0903772791875753e-2 |
Outlying measurements have moderate (0.18749999999999997%) 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.