criterion performance measurements
overview
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|V|=4039 |E|=88234 |SCC|=4039/AM-alga
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 0.2034752241565406 | 0.20912102803736665 | 0.22505968573063406 |
| Standard deviation | 1.0087537095510406e-3 | 1.2707704934846091e-2 | 1.7792965253347257e-2 |
Outlying measurements have moderate (0.14707698219695797%) effect on estimated standard deviation.
|V|=4039 |E|=88234 |SCC|=4039/AIM-alga
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 0.17052815498189175 | 0.1759487578098843 | 0.19035268011405906 |
| Standard deviation | 2.0122425956417157e-3 | 1.1661843344457926e-2 | 1.6508350145149196e-2 |
Outlying measurements have moderate (0.15020005367050143%) effect on estimated standard deviation.
|V|=4039 |E|=88234 |SCC|=4039/KL-alga
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 0.43248935710289516 | 0.43386998644806835 | 0.43505610202555545 |
| Standard deviation | 7.37664396804891e-4 | 1.4983087153809082e-3 | 1.9758811627187097e-3 |
Outlying measurements have moderate (0.1875%) effect on estimated standard deviation.
|V|=81306 |E|=1768149 |SCC|=12248/AM-alga
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 7.363561361577013 | 7.43648039510784 | 7.487780909995005 |
| Standard deviation | 3.797620124214011e-2 | 7.40025717767056e-2 | 0.10318774789721166 |
Outlying measurements have moderate (0.18749999999999997%) effect on estimated standard deviation.
|V|=81306 |E|=1768149 |SCC|=12248/AIM-alga
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 4.386934412473541 | 4.473736984689215 | 4.615234728436917 |
| Standard deviation | 8.06830385678925e-3 | 0.13377099961864058 | 0.16742167281331508 |
Outlying measurements have moderate (0.1875%) effect on estimated standard deviation.
|V|=81306 |E|=1768149 |SCC|=12248/KL-alga
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 58.285805520485155 | 60.73361150976174 | 61.55852202726722 |
| Standard deviation | 0.10169633406311451 | 1.6428363319403216 | 2.0067455714196987 |
Outlying measurements have moderate (0.1875%) 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.