criterion performance measurements
overview
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270 275 280 285 290 295 300 305 310
mean |
2 3 4 5 1 iters 500 750 0 s 250 ms 1 s 1.25 1.5
|
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | 233 ms | 282 ms | 333 ms |
| R² goodness-of-fit | 0.947 | 0.990 | 1.000 |
| Mean execution time | 272 ms | 281 ms | 301 ms |
| Standard deviation | 538 μs | 15.5 ms | 20.0 ms |
Outlying measurements have moderate (16.0%) effect on estimated standard deviation.
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10.4 10.6 10.8 11.0 11.2 11.4
mean |
10 15 20 25 30 5 iters 100 150 200 250 300 350 0 s 50 ms
|
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | 10.7 ms | 10.9 ms | 11.1 ms |
| R² goodness-of-fit | 0.996 | 0.998 | 1.000 |
| Mean execution time | 10.8 ms | 10.8 ms | 10.9 ms |
| Standard deviation | 162 μs | 228 μs | 329 μs |
Outlying measurements have slight (3.3%) effect on estimated standard deviation.
readByteString
9 10 8.5 9.5
mean |
10 15 20 25 30 35 5 iters 100 150 200 250 300 350 0 s 50 ms
|
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | 8.61 ms | 8.90 ms | 9.44 ms |
| R² goodness-of-fit | 0.981 | 0.990 | 0.999 |
| Mean execution time | 8.84 ms | 8.94 ms | 9.13 ms |
| Standard deviation | 230 μs | 380 μs | 588 μs |
Outlying measurements have moderate (17.8%) 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.