criterion performance measurements
overview
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map/inline
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 3.7186988948217686e-4 | 3.726765330711036e-4 | 3.766412045885275e-4 |
| Standard deviation | 1.0504752840610923e-7 | 5.159948730825623e-6 | 1.1846179234395934e-5 |
Outlying measurements have slight (6.264164410733519e-2%) effect on estimated standard deviation.
map/transformers
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 3.7174953365171895e-4 | 3.7177055946477117e-4 | 3.7181216296632377e-4 |
| Standard deviation | 4.7597661096306695e-8 | 9.178362410598543e-8 | 1.6403741617400122e-7 |
Outlying measurements have slight (1.0987654320987656e-2%) effect on estimated standard deviation.
map/transformers+inline
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 3.7252842017052485e-4 | 3.734591868598133e-4 | 3.7701745480403205e-4 |
| Standard deviation | 1.3395022951035307e-6 | 5.082602172477005e-6 | 1.0994490301064082e-5 |
Outlying measurements have slight (6.250076458751079e-2%) effect on estimated standard deviation.
drop/inline
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 0.1034582396278525 | 0.1055111203905222 | 0.10769065257103427 |
| Standard deviation | 2.4590985082454738e-3 | 3.3988953948084857e-3 | 4.6854418695683984e-3 |
Outlying measurements have slight (9.876543209876536e-2%) effect on estimated standard deviation.
drop/transformers
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 0.5116518669512016 | 0.5176712583259352 | 0.5214209607758926 |
| Standard deviation | 0.0 | 5.6659769210421e-3 | 6.494675156591758e-3 |
Outlying measurements have moderate (0.1875%) effect on estimated standard deviation.
drop/transformers-inline
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 0.10330804545821012 | 0.10541088707741829 | 0.10765183778865502 |
| Standard deviation | 2.4373722215995106e-3 | 3.5240376668613776e-3 | 4.922742262406657e-3 |
Outlying measurements have slight (9.876543209876543e-2%) effect on estimated standard deviation.
map . drop . map/inline
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 0.26140520189460575 | 0.27407030427278617 | 0.29170050349392507 |
| Standard deviation | 4.393071091553041e-3 | 1.812180789854594e-2 | 2.4176119514871905e-2 |
Outlying measurements have moderate (0.1714088955402361%) effect on estimated standard deviation.
map . drop . map/transformers
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 0.9591676264756849 | 0.9853377094647732 | 1.0011427389620435 |
| Standard deviation | 0.0 | 2.4088170236790437e-2 | 2.7375114104396994e-2 |
Outlying measurements have moderate (0.18749999999999997%) effect on estimated standard deviation.
map . drop . map/transformers-inline
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 0.2609030828719366 | 0.26834075192288964 | 0.2736855220530654 |
| Standard deviation | 2.493166327410784e-3 | 7.3425661906146725e-3 | 1.0027710707687364e-2 |
Outlying measurements have moderate (0.16%) 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.