criterion performance measurements
overview
want to understand this report?
testTerms/0
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 6.501195367491666e-7 | 6.519331659136747e-7 | 6.541644680498362e-7 |
| Standard deviation | 5.420841622621774e-9 | 6.608817105173806e-9 | 9.38453629522354e-9 |
Outlying measurements have slight (7.719164866710199e-2%) effect on estimated standard deviation.
testTerms/1
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 1.984706254162783e-6 | 1.9904768459783663e-6 | 1.9963083990098768e-6 |
| Standard deviation | 1.6932530395028555e-8 | 1.8940692961384465e-8 | 2.1155278879300756e-8 |
Outlying measurements have slight (6.217540950463195e-2%) effect on estimated standard deviation.
testTerms/2
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 6.100596680795778e-6 | 6.120161831642877e-6 | 6.140738167208487e-6 |
| Standard deviation | 5.4807925593116275e-8 | 6.875716294331063e-8 | 8.265741428014847e-8 |
Outlying measurements have slight (7.585383020668056e-2%) effect on estimated standard deviation.
testTerms/3
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 1.85604466400344e-5 | 1.86103329084214e-5 | 1.868401646574174e-5 |
| Standard deviation | 1.4148531642921407e-7 | 1.9368746823491435e-7 | 2.756008025338465e-7 |
Outlying measurements have slight (5.067002835639426e-2%) effect on estimated standard deviation.
testTerms/4
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 5.5788103257657845e-5 | 5.583398405715048e-5 | 5.588692024249874e-5 |
| Standard deviation | 1.3761057493603553e-7 | 1.6464975530451763e-7 | 2.0077786268441186e-7 |
Outlying measurements have no (7.691845441980519e-3%) 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.