criterion performance measurements
overview
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| alpmestan.com | |
| links/50 | |
| links/500 | |
| links/5000 |
alpmestan.com/tagsoup
5.0 ms 5.5 6.0 6.5 7.0 7.5 8.0
mean |
5.0 ms 5.5 6.0 6.5 7.0 7.5 8.0
|
| lower bound | estimate | upper bound | |
|---|---|---|---|
| Mean execution time | 5.619 ms | 5.763 ms | 5.938 ms |
| Standard deviation | 688.1 μs | 806.8 μs | 926.9 μs |
Outlying measurements have severe (88.4%) effect on estimated standard deviation.
alpmestan.com/taggy
1.50 ms 1.75 2.00 2.25 2.50 2.75
mean |
1.50 ms 1.75 2.00 2.25 2.50 2.75
|
| lower bound | estimate | upper bound | |
|---|---|---|---|
| Mean execution time | 1.711 ms | 1.766 ms | 1.835 ms |
| Standard deviation | 262.9 μs | 314.1 μs | 374.7 μs |
Outlying measurements have severe (92.6%) effect on estimated standard deviation.
links/50/tagsoup
0.9 ms 1.0 1.1 1.2 1.3 1.4 1.5 1.6
mean |
0.9 ms 1.0 1.1 1.2 1.3 1.4 1.5 1.6
|
| lower bound | estimate | upper bound | |
|---|---|---|---|
| Mean execution time | 1.000 ms | 1.027 ms | 1.063 ms |
| Standard deviation | 127.5 μs | 159.9 μs | 199.3 μs |
Outlying measurements have severe (90.5%) effect on estimated standard deviation.
links/50/taggy
350 μs 400 450 500 550 600 650 700 750
mean |
350 μs 400 450 500 550 600 650 700 750
|
| lower bound | estimate | upper bound | |
|---|---|---|---|
| Mean execution time | 433.7 μs | 447.4 μs | 464.6 μs |
| Standard deviation | 66.03 μs | 79.07 μs | 97.30 μs |
Outlying measurements have severe (92.6%) effect on estimated standard deviation.
links/500/tagsoup
13 ms 14 15 16 17 18 19
mean |
13 ms 14 15 16 17 18 19
|
| lower bound | estimate | upper bound | |
|---|---|---|---|
| Mean execution time | 13.99 ms | 14.28 ms | 14.61 ms |
| Standard deviation | 1.365 ms | 1.573 ms | 1.823 ms |
Outlying measurements have severe (82.1%) effect on estimated standard deviation.
links/500/taggy
4.0 ms 4.5 5.0 5.5 6.0 6.5 7.0 7.5
mean |
4.0 ms 4.5 5.0 5.5 6.0 6.5 7.0 7.5
|
| lower bound | estimate | upper bound | |
|---|---|---|---|
| Mean execution time | 4.312 ms | 4.438 ms | 4.597 ms |
| Standard deviation | 605.5 μs | 722.9 μs | 901.5 μs |
Outlying measurements have severe (91.5%) effect on estimated standard deviation.
links/5000/tagsoup
170 ms 175 180 185 190
mean |
170 ms 175 180 185 190
|
| lower bound | estimate | upper bound | |
|---|---|---|---|
| Mean execution time | 180.3 ms | 181.2 ms | 182.1 ms |
| Standard deviation | 4.164 ms | 4.673 ms | 5.314 ms |
Outlying measurements have moderate (20.0%) effect on estimated standard deviation.
links/5000/taggy
50.0 ms 52.5 55.0 57.5 60.0 62.5 65.0
mean |
50.0 ms 52.5 55.0 57.5 60.0 62.5 65.0
|
| lower bound | estimate | upper bound | |
|---|---|---|---|
| Mean execution time | 55.64 ms | 56.27 ms | 56.89 ms |
| Standard deviation | 2.856 ms | 3.223 ms | 3.689 ms |
Outlying measurements have severe (55.5%) effect on estimated standard deviation.
understanding this report
In this report, each function benchmarked by criterion is assigned a section of its own. In each section, we display two charts, each with an x axis that represents measured execution time. These charts 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. Measurements are displayed on the y axis in the order in which they occurred.
Under the charts is a small table displaying the mean and standard deviation of the measurements. We use a statistical technique called the bootstrap to provide confidence intervals on our estimates of these values. The bootstrap-derived upper and lower bounds on the mean and standard deviation 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.