criterion performance measurements
overview
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MT/IO
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 1.8291468746220826e-5 | 1.8596057046279216e-5 | 1.8970823848657063e-5 |
| Standard deviation | 9.730051056197268e-7 | 1.1787689334164623e-6 | 1.5106340561956564e-6 |
Outlying measurements have severe (0.6956780034152097%) effect on estimated standard deviation.
MT/SFMT
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 1.753139827029027e-5 | 1.787140244531512e-5 | 1.8293916338891704e-5 |
| Standard deviation | 9.698150874538832e-7 | 1.212543485037915e-6 | 1.6551725975111806e-6 |
Outlying measurements have severe (0.7247180493076752%) effect on estimated standard deviation.
MWC
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 2.4451169186335697e-5 | 2.4851588745848846e-5 | 2.5408326194629685e-5 |
| Standard deviation | 1.1644412276095319e-6 | 1.5793701632470701e-6 | 2.3187687446279647e-6 |
Outlying measurements have severe (0.6856970481740042%) effect on estimated standard deviation.
baseline
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 1.0848677037684808e-5 | 1.1025053189902466e-5 | 1.119338596159492e-5 |
| Standard deviation | 5.21740915340408e-7 | 5.985011697254888e-7 | 7.107161713257431e-7 |
Outlying measurements have severe (0.6403084080565015%) 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.