criterion performance measurements
overview
want to understand this report?
enum1/f1/aug
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 3.19153941012956e-3 | 3.227001950163621e-3 | 3.255616403616459e-3 |
| Standard deviation | 7.891608828857235e-5 | 9.869468277435015e-5 | 1.308040000639304e-4 |
Outlying measurements have moderate (0.12930411198828268%) effect on estimated standard deviation.
enum1/f1/tree
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 3.194149738164282e-3 | 3.234290256466184e-3 | 3.2684753478545033e-3 |
| Standard deviation | 9.065555213430596e-5 | 1.1129949859592542e-4 | 1.3610868598290795e-4 |
Outlying measurements have moderate (0.1719049893169475%) effect on estimated standard deviation.
enum1/f2/aug
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 3.3620458592905538e-3 | 3.402957500538141e-3 | 3.438127461760807e-3 |
| Standard deviation | 9.703231020617242e-5 | 1.1477718505798154e-4 | 1.3799003638317032e-4 |
Outlying measurements have moderate (0.15432597661818032%) effect on estimated standard deviation.
enum1/f2/tree
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 2.876256739611572e-3 | 2.9198533980781143e-3 | 2.9553544215250483e-3 |
| Standard deviation | 9.762841433296408e-5 | 1.2172479510301701e-4 | 1.5104008474210086e-4 |
Outlying measurements have moderate (0.2279541903837642%) effect on estimated standard deviation.
ints/f1Int/aug
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 3.1901520623537722e-3 | 3.2275832321472906e-3 | 3.2662577175477346e-3 |
| Standard deviation | 1.0124146947490757e-4 | 1.1658130063417458e-4 | 1.3771591602265457e-4 |
Outlying measurements have moderate (0.1728203249737228%) effect on estimated standard deviation.
ints/f1Int/tree
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 3.0540557800383533e-3 | 3.089595409324678e-3 | 3.125210255569315e-3 |
| Standard deviation | 9.392954797834751e-5 | 1.1145015861363375e-4 | 1.3685920464940525e-4 |
Outlying measurements have moderate (0.18932643881302613%) effect on estimated standard deviation.
ints/f2Int/aug
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 3.088379311427208e-3 | 3.1281944423044235e-3 | 3.1620024086840954e-3 |
| Standard deviation | 9.114666682096204e-5 | 1.1380619746868417e-4 | 1.4008260489156035e-4 |
Outlying measurements have moderate (0.18950643255497024%) effect on estimated standard deviation.
ints/f2Int/tree
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 2.7277840815357287e-3 | 2.7644042978050344e-3 | 2.795004775203611e-3 |
| Standard deviation | 8.340926316589819e-5 | 1.0147655719099366e-4 | 1.3910903786618322e-4 |
Outlying measurements have moderate (0.20159737325638113%) 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.