criterion performance measurements
overview
want to understand this report?
hashMap
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 1.1079337686668929 | 1.1118317350581899 | 1.1155279756712846 |
| Standard deviation | 0.0 | 6.2347239582959705e-3 | 6.402076538879691e-3 |
Outlying measurements have moderate (0.1875%) effect on estimated standard deviation.
lazyHashMap
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 0.6050280515104536 | 0.648151868254354 | 0.6811688430845001 |
| Standard deviation | 0.0 | 5.07514985589225e-2 | 5.718707791803603e-2 |
Outlying measurements have moderate (0.2101804941984909%) effect on estimated standard deviation.
treeMap
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 0.6176181022485254 | 0.6234788088583932 | 0.6346098493319007 |
| Standard deviation | 0.0 | 9.644282322289024e-3 | 9.81018975258874e-3 |
Outlying measurements have moderate (0.1875%) effect on estimated standard deviation.
lazyTreeMap
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 0.7115394046646545 | 0.7382926173071612 | 0.7615077150888588 |
| Standard deviation | 0.0 | 3.752284661345008e-2 | 4.020972886057987e-2 |
Outlying measurements have moderate (0.1875%) effect on estimated standard deviation.
trieMap
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 0.9874362552538516 | 1.016644161750123 | 1.0402545899961197 |
| Standard deviation | 0.0 | 3.701183160024759e-2 | 4.089446131052591e-2 |
Outlying measurements have moderate (0.1875%) effect on estimated standard deviation.
group . sort
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 0.38601253600209046 | 0.398264101588575 | 0.4043930002590062 |
| Standard deviation | 0.0 | 1.0610171151690466e-2 | 1.0615563891628281e-2 |
Outlying measurements have moderate (0.18749999999999994%) effect on estimated standard deviation.
nub
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 2.146072198999658 | 2.1540133390033995 | 2.159312120340474 |
| Standard deviation | 0.0 | 7.94818133776119e-3 | 9.177758494010351e-3 |
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.