criterion performance measurements
overview
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pyth/ndet/mtl
lower bound | estimate | upper bound | |
---|---|---|---|
OLS regression | xxx | xxx | xxx |
R² goodness-of-fit | xxx | xxx | xxx |
Mean execution time | 3.1296677394245935e-4 | 3.131659393506705e-4 | 3.138248689171451e-4 |
Standard deviation | 4.4905954592977045e-7 | 1.082045292054602e-6 | 2.394427081755867e-6 |
Outlying measurements have slight (1.0525124490719781e-2%) effect on estimated standard deviation.
pyth/ndet/eff
lower bound | estimate | upper bound | |
---|---|---|---|
OLS regression | xxx | xxx | xxx |
R² goodness-of-fit | xxx | xxx | xxx |
Mean execution time | 5.765862115382986e-2 | 5.7724624861021166e-2 | 5.7831047269477104e-2 |
Standard deviation | 6.674830132101631e-5 | 1.4695388253674097e-4 | 1.9874819288508882e-4 |
Outlying measurements have slight (7.638888888888888e-2%) effect on estimated standard deviation.
pyth/ndet : st/mtl
lower bound | estimate | upper bound | |
---|---|---|---|
OLS regression | xxx | xxx | xxx |
R² goodness-of-fit | xxx | xxx | xxx |
Mean execution time | 0.16786697004760126 | 0.17411292817007443 | 0.1791173460714693 |
Standard deviation | 6.200043968794704e-3 | 8.226276133458112e-3 | 1.1341173659211225e-2 |
Outlying measurements have moderate (0.12245003885656061%) effect on estimated standard deviation.
pyth/ndet : st/eff
lower bound | estimate | upper bound | |
---|---|---|---|
OLS regression | xxx | xxx | xxx |
R² goodness-of-fit | xxx | xxx | xxx |
Mean execution time | 0.16963141483944053 | 0.16983360530204153 | 0.17039271496191327 |
Standard deviation | 7.932014476160957e-5 | 4.731140387192827e-4 | 7.079808580216453e-4 |
Outlying measurements have moderate (0.12244897959183669%) 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.