criterion performance measurements
overview
want to understand this report?
filterWords/hsSolutionBasic
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 5.426248792053743e-5 | 5.495245875232887e-5 | 5.6405443346403236e-5 |
| Standard deviation | 1.0688497497513093e-6 | 3.12288054862363e-6 | 5.189824610249113e-6 |
Outlying measurements have severe (0.6057307508694613%) effect on estimated standard deviation.
filterWords/hsSolutionBonus
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 6.283854219794763e-5 | 6.311962143963653e-5 | 6.35070062628974e-5 |
| Standard deviation | 8.457527935397212e-7 | 1.1527915949528087e-6 | 1.5327509594373672e-6 |
Outlying measurements have moderate (0.13526995968261174%) effect on estimated standard deviation.
filterWords/pre-compiled hsSolutionBasic
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 5.431925376693129e-5 | 5.4795212365019375e-5 | 5.560061797554016e-5 |
| Standard deviation | 1.2566191416511856e-6 | 1.9195413286443066e-6 | 3.2739005731425467e-6 |
Outlying measurements have moderate (0.365604195257131%) effect on estimated standard deviation.
filterWords/pre-compiled hsSolutionBonus
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 6.273999144291073e-5 | 6.316456276727373e-5 | 6.364468802040656e-5 |
| Standard deviation | 1.200514453849295e-6 | 1.5306263054010009e-6 | 2.0490066971260968e-6 |
Outlying measurements have moderate (0.21179345206241504%) 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.