criterion performance measurements
overview
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| map | |
| drop | |
| map . drop . map |
map/inline
370 375 380 385 390 395 400 405
mean |
200 300 400 500 600 100 iters 100 150 200 250 0 s 50 ms
|
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | 372 μs | 376 μs | 384 μs |
| R² goodness-of-fit | 0.998 | 0.999 | 1.000 |
| Mean execution time | 372 μs | 373 μs | 377 μs |
| Standard deviation | 105 ns | 5.16 μs | 11.8 μs |
Outlying measurements have slight (6.3%) effect on estimated standard deviation.
map/transformers
372 372 372 372 372 372
mean |
200 300 400 500 600 100 iters 100 150 200 250 0 s 50 ms
|
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | 372 μs | 372 μs | 372 μs |
| R² goodness-of-fit | 1.000 | 1.000 | 1.000 |
| Mean execution time | 372 μs | 372 μs | 372 μs |
| Standard deviation | 47.6 ns | 91.8 ns | 164 ns |
Outlying measurements have slight (1.1%) effect on estimated standard deviation.
map/transformers+inline
370 375 380 385 390 395 400 405
mean |
200 300 400 500 600 100 iters 100 150 200 250 0 s 50 ms
|
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | 372 μs | 375 μs | 381 μs |
| R² goodness-of-fit | 0.998 | 0.999 | 1.000 |
| Mean execution time | 373 μs | 373 μs | 377 μs |
| Standard deviation | 1.34 μs | 5.08 μs | 11.0 μs |
Outlying measurements have slight (6.3%) effect on estimated standard deviation.
drop/inline
100 102 104 106 108 110
mean |
2 3 4 5 6 7 8 9 1 iters 400 600 800 0 s 200 ms 1 s
|
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | 99.6 ms | 105 ms | 112 ms |
| R² goodness-of-fit | 0.991 | 0.995 | 0.999 |
| Mean execution time | 103 ms | 106 ms | 108 ms |
| Standard deviation | 2.46 ms | 3.40 ms | 4.69 ms |
Outlying measurements have slight (9.9%) effect on estimated standard deviation.
drop/transformers
512 514 516 518 520 522 524
mean |
1 2 2 3 3 4 4 0.5 iters 2 0 s 500 ms 1 s 1.5 2.5
|
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | 439 ms | 501 ms | 545 ms |
| R² goodness-of-fit | 0.994 | 0.998 | 1.000 |
| Mean execution time | 512 ms | 518 ms | 521 ms |
| Standard deviation | 0 s | 5.67 ms | 6.49 ms |
Outlying measurements have moderate (18.8%) effect on estimated standard deviation.
drop/transformers-inline
100 102 104 106 108 110
mean |
2 3 4 5 6 7 8 9 1 iters 400 600 800 0 s 200 ms 1 s
|
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | 99.4 ms | 105 ms | 111 ms |
| R² goodness-of-fit | 0.990 | 0.995 | 0.999 |
| Mean execution time | 103 ms | 105 ms | 108 ms |
| Standard deviation | 2.44 ms | 3.52 ms | 4.92 ms |
Outlying measurements have slight (9.9%) effect on estimated standard deviation.
map . drop . map/inline
260 270 280 290 300
mean |
2 3 4 5 1 iters 2 0 s 500 ms 1 s 1.5
|
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | 226 ms | 287 ms | 358 ms |
| R² goodness-of-fit | 0.975 | 0.978 | 1.000 |
| Mean execution time | 261 ms | 274 ms | 292 ms |
| Standard deviation | 4.39 ms | 18.1 ms | 24.2 ms |
Outlying measurements have moderate (17.1%) effect on estimated standard deviation.
map . drop . map/transformers
960 970 980 990 1 s 1.01
mean |
1 2 2 3 3 4 4 0.5 iters 1 2 3 4 0 s
|
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | 817 ms | 932 ms | 1.04 s |
| R² goodness-of-fit | 0.993 | 0.998 | 1.000 |
| Mean execution time | 959 ms | 985 ms | 1.00 s |
| Standard deviation | 0 s | 24.1 ms | 27.4 ms |
Outlying measurements have moderate (18.7%) effect on estimated standard deviation.
map . drop . map/transformers-inline
260 265 270 275
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 | 227 ms | 264 ms | 291 ms |
| R² goodness-of-fit | 0.982 | 0.992 | 1.000 |
| Mean execution time | 261 ms | 268 ms | 274 ms |
| Standard deviation | 2.49 ms | 7.34 ms | 10.0 ms |
Outlying measurements have moderate (16.0%) 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.