criterion performance measurements
overview
want to understand this report?
testTerms/0
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 7.896233104562074e-4 | 7.978043575981287e-4 | 8.15375444173389e-4 |
| Standard deviation | 2.4508000469402396e-5 | 4.0005706551468544e-5 | 6.322710215415667e-5 |
Outlying measurements have moderate (0.4115837576723002%) effect on estimated standard deviation.
testTerms/1
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 3.0209950631119903e-3 | 3.0478385882019457e-3 | 3.0987444425706855e-3 |
| Standard deviation | 8.256235190239576e-5 | 1.2394957608157742e-4 | 2.1184102104383125e-4 |
Outlying measurements have moderate (0.2291165561717985%) effect on estimated standard deviation.
testTerms/2
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 9.662843589283225e-3 | 9.736175391989727e-3 | 9.815492098488076e-3 |
| Standard deviation | 1.4324273554278485e-4 | 2.1013379512942103e-4 | 3.318961580205219e-4 |
Outlying measurements have slight (3.1217481789802114e-2%) effect on estimated standard deviation.
testTerms/3
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 2.9025647226898373e-2 | 2.9160915091661972e-2 | 2.92525508366552e-2 |
| Standard deviation | 1.4500613361436828e-4 | 2.3745111515713008e-4 | 4.172415047972088e-4 |
Outlying measurements have slight (5.246913580246886e-2%) effect on estimated standard deviation.
testTerms/4
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 8.949004111111063e-2 | 9.020348111111105e-2 | 9.054852055555562e-2 |
| Standard deviation | 2.937661073706099e-4 | 8.176424195094971e-4 | 1.2017839716264543e-3 |
Outlying measurements have slight (8.999999999999998e-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.