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 | 1.8650410857348864e-5 | 1.9038933995073976e-5 | 1.9533665002645196e-5 |
Standard deviation | 1.1730855569651646e-6 | 1.5149097514494067e-6 | 2.188220143537585e-6 |
Outlying measurements have severe (0.7843799973151266%) 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 | 2.4415070639659767e-5 | 2.4889361373229672e-5 | 2.551162577573722e-5 |
Standard deviation | 1.3242820592619574e-6 | 1.7019760584954395e-6 | 2.4272911687439043e-6 |
Outlying measurements have severe (0.7205839459651122%) 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 | 1.0754155290507643e-5 | 1.0959148182618883e-5 | 1.1203205839752294e-5 |
Standard deviation | 6.588819074374334e-7 | 7.734756840594912e-7 | 9.72516616405865e-7 |
Outlying measurements have severe (0.7540877747915276%) 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.