criterion performance measurements
overview
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glob-posix/Simple path
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 2.0145041478627266e-6 | 2.032194954707462e-6 | 2.060233765330293e-6 |
| Standard deviation | 5.2061029873083364e-8 | 7.33526352902582e-8 | 9.491189412641596e-8 |
Outlying measurements have moderate (0.48716392396087727%) effect on estimated standard deviation.
glob-posix/Python versions
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 5.634485550538572e-5 | 5.6556785545507134e-5 | 5.6911913187751685e-5 |
| Standard deviation | 6.066963704086786e-7 | 8.730223287762466e-7 | 1.3585449009303944e-6 |
Outlying measurements have moderate (0.10253990512705576%) effect on estimated standard deviation.
glob-posix/Python site
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 5.457012491295437e-5 | 5.538668603437231e-5 | 5.8703831673128576e-5 |
| Standard deviation | 3.3341266494923234e-7 | 4.418075277824929e-6 | 9.733461413292414e-6 |
Outlying measurements have severe (0.7543792918860517%) effect on estimated standard deviation.
MissingH/Simple path
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 2.0180379424399e-6 | 2.1941914883992915e-6 | 2.3601570086402334e-6 |
| Standard deviation | 5.223389506069342e-7 | 5.891090662212737e-7 | 7.45019721356891e-7 |
Outlying measurements have severe (0.9837866638317999%) effect on estimated standard deviation.
MissingH/Python versions
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 2.2673401010552956e-4 | 2.3022080538731348e-4 | 2.3598510049553342e-4 |
| Standard deviation | 1.0541358782057611e-5 | 1.4982081505610456e-5 | 1.9935665365657026e-5 |
Outlying measurements have severe (0.6153564642465809%) effect on estimated standard deviation.
MissingH/Python site
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 1.088508312749642e-3 | 1.1268450608713865e-3 | 1.198300127607555e-3 |
| Standard deviation | 1.2754192375543258e-4 | 1.7852449782809125e-4 | 2.452761941119994e-4 |
Outlying measurements have severe (0.8770067359230508%) effect on estimated standard deviation.
Glob/Simple path
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 4.621570943388557e-3 | 4.665443737789015e-3 | 4.766592037370835e-3 |
| Standard deviation | 1.0871561771742286e-4 | 1.9864997242092144e-4 | 3.789370944051207e-4 |
Outlying measurements have moderate (0.22646252412547696%) effect on estimated standard deviation.
Glob/Python versions
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 2.1254096183597748e-2 | 2.148292625321264e-2 | 2.217762029617671e-2 |
| Standard deviation | 4.0305802722126054e-4 | 8.292161917979924e-4 | 1.5164464406536649e-3 |
Outlying measurements have moderate (0.13266522349657034%) effect on estimated standard deviation.
Glob/Python site
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 3.363978986117748e-2 | 3.3772590183338565e-2 | 3.4048613458673015e-2 |
| Standard deviation | 1.6606167632308118e-4 | 3.599295335804036e-4 | 6.451102050558873e-4 |
Outlying measurements have slight (5.859374999999999e-2%) 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.