criterion performance measurements
overview
want to understand this report?
reverse/library reverse
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 9.716982987248993e-6 | 9.9180780297361e-6 | 1.0447278183988108e-5 |
| Standard deviation | 1.3388781614736357e-7 | 9.598205750089096e-7 | 1.8853978503660426e-6 |
Outlying measurements have severe (0.8522363781116609%) effect on estimated standard deviation.
reverse/simple reverse
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 5.911826185641671e-3 | 6.047827167612605e-3 | 6.321378939453348e-3 |
| Standard deviation | 3.1389398214789555e-4 | 5.20801130812429e-4 | 7.509912061706823e-4 |
Outlying measurements have severe (0.5109560399504653%) effect on estimated standard deviation.
reverse/fast reverse
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 9.376768723899086e-6 | 9.49132970987831e-6 | 9.731205130877708e-6 |
| Standard deviation | 3.3333059152460446e-7 | 5.502590589383045e-7 | 8.651601660204759e-7 |
Outlying measurements have severe (0.6774598549149854%) 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.