criterion performance measurements
overview
want to understand this report?
Powerline/Left prompt
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 0.24284364357254754 | 0.24641625939341924 | 0.24887820716825712 |
| Standard deviation | 2.7012134445468686e-3 | 4.423696189251499e-3 | 6.5047742539705595e-3 |
Outlying measurements have moderate (0.10937499999999993%) effect on estimated standard deviation.
Powerline/Right prompt
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 1.0074456392775128 | 1.03198561030068 | 1.0498325998469618 |
| Standard deviation | 0.0 | 2.704781690618988e-2 | 3.091189265631063e-2 |
Outlying measurements have moderate (0.18749999999999994%) effect on estimated standard deviation.
Powerline-hs/Left prompt
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 3.608341057616666e-2 | 3.628096440577019e-2 | 3.652366047442225e-2 |
| Standard deviation | 4.195208244402976e-4 | 5.308780406098251e-4 | 6.923744582812634e-4 |
Outlying measurements have slight (4.158790170132291e-2%) effect on estimated standard deviation.
Powerline-hs/Right prompt
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 4.248928627465102e-2 | 4.2824948610483124e-2 | 4.438978631709065e-2 |
| Standard deviation | 3.4443902969417056e-4 | 1.2901386110153496e-3 | 2.6323125528603937e-3 |
Outlying measurements have slight (8.78020317629907e-2%) effect on estimated standard deviation.
Python Hello World
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 2.7858858679526758e-2 | 2.7987811228452085e-2 | 2.820868685216801e-2 |
| Standard deviation | 2.824230848069033e-4 | 4.2179254185070405e-4 | 6.507634376514367e-4 |
Outlying measurements have slight (3.698224852070995e-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.