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 8.5 9.0
mean |
5.0 ms 5.5 6.0 6.5 7.0 7.5 8.0 8.5 9.0
|
| lower bound | estimate | upper bound | |
|---|---|---|---|
| Mean execution time | 5.566 ms | 5.706 ms | 5.882 ms |
| Standard deviation | 672.2 μs | 801.6 μs | 987.7 μs |
Outlying measurements have severe (88.4%) effect on estimated standard deviation.
alpmestan.com/taggy
1.2 ms 1.4 1.6 1.8 2.0
mean |
1.2 ms 1.4 1.6 1.8 2.0
|
| lower bound | estimate | upper bound | |
|---|---|---|---|
| Mean execution time | 1.324 ms | 1.365 ms | 1.415 ms |
| Standard deviation | 197.3 μs | 232.3 μs | 273.0 μs |
Outlying measurements have severe (92.5%) effect on estimated standard deviation.
links/50/tagsoup
0.8 ms 1.0 1.2 1.4 1.6 1.8 2.0
mean |
0.8 ms 1.0 1.2 1.4 1.6 1.8 2.0
|
| lower bound | estimate | upper bound | |
|---|---|---|---|
| Mean execution time | 986.1 μs | 1.012 ms | 1.055 ms |
| Standard deviation | 118.8 μs | 167.2 μs | 265.0 μs |
Outlying measurements have severe (91.5%) effect on estimated standard deviation.
links/50/taggy
300 μs 350 400 450 500
mean |
300 μs 350 400 450 500
|
| lower bound | estimate | upper bound | |
|---|---|---|---|
| Mean execution time | 333.9 μs | 343.1 μs | 355.2 μs |
| Standard deviation | 43.43 μs | 53.52 μs | 65.28 μs |
Outlying measurements have severe (90.5%) effect on estimated standard deviation.
links/500/tagsoup
12.0 ms 13.0 14.0 15.0 16.0 17.0 18.0
mean |
12.0 ms 13.0 14.0 15.0 16.0 17.0 18.0
|
| lower bound | estimate | upper bound | |
|---|---|---|---|
| Mean execution time | 13.59 ms | 13.85 ms | 14.16 ms |
| Standard deviation | 1.288 ms | 1.466 ms | 1.656 ms |
Outlying measurements have severe (81.1%) effect on estimated standard deviation.
links/500/taggy
3.0 ms 3.5 4.0 4.5
mean |
3.0 ms 3.5 4.0 4.5
|
| lower bound | estimate | upper bound | |
|---|---|---|---|
| Mean execution time | 3.111 ms | 3.189 ms | 3.290 ms |
| Standard deviation | 364.8 μs | 451.2 μs | 544.1 μs |
Outlying measurements have severe (88.4%) 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 | 179.2 ms | 180.2 ms | 181.2 ms |
| Standard deviation | 4.394 ms | 4.929 ms | 5.618 ms |
Outlying measurements have moderate (21.9%) effect on estimated standard deviation.
links/5000/taggy
34 ms 36 38 40 42 44
mean |
34 ms 36 38 40 42 44
|
| lower bound | estimate | upper bound | |
|---|---|---|---|
| Mean execution time | 36.71 ms | 37.27 ms | 37.84 ms |
| Standard deviation | 2.641 ms | 2.882 ms | 3.193 ms |
Outlying measurements have severe (69.7%) 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.