criterion performance measurements
overview
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computer/reddit/sum-of-primes 100000
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 0.34364515575483284 | 0.3476947453576334 | 0.3522461510801804 |
| Standard deviation | 2.0274077396607026e-3 | 5.059438215663372e-3 | 7.011077780637158e-3 |
Outlying measurements have moderate (0.1875%) effect on estimated standard deviation.
computer/reddit/ackerman 3,6
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 3.9851388474172474e-2 | 4.120716382018168e-2 | 4.3732079380016484e-2 |
| Standard deviation | 1.0812059253529072e-3 | 3.552659700619036e-3 | 5.814524763340409e-3 |
Outlying measurements have moderate (0.3177620471474721%) effect on estimated standard deviation.
computer/reddit/isqrt 130
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 1.9279457934005234e-4 | 1.9642801156340416e-4 | 2.0194340845087103e-4 |
| Standard deviation | 1.0171290035684446e-5 | 1.4799867254835714e-5 | 2.0641142326049944e-5 |
Outlying measurements have severe (0.6859714905931146%) effect on estimated standard deviation.
computer/reddit/factor 19338240
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 3.261409350026432e-2 | 3.291361056994155e-2 | 3.338360406904353e-2 |
| Standard deviation | 5.478720709232755e-4 | 8.065416599348573e-4 | 1.1391033339465749e-3 |
Outlying measurements have slight (5.859374999999999e-2%) effect on estimated standard deviation.
computer/reddit/factor 2147483647
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 0.8958079908552463 | 0.9131821994111912 | 0.9366416933771688 |
| Standard deviation | 7.59616896739352e-3 | 2.361196495792182e-2 | 3.185448840715943e-2 |
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.