criterion performance measurements
overview
want to understand this report?
sum $ map (+ 1) $ map (* 2) $ enumFromTo 1 9001/vegito
5 4.95 5.05 5.10 5.15 5.2 5.25 5.30
mean |
10 15 20 25 30 35 40 45 5×10³ iters 100 150 200 250 0 s 50 ms
|
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | 5.05 μs | 5.06 μs | 5.07 μs |
| R² goodness-of-fit | 1.000 | 1.000 | 1.000 |
| Mean execution time | 5.05 μs | 5.06 μs | 5.08 μs |
| Standard deviation | 27.6 ns | 46.5 ns | 87.6 ns |
Outlying measurements have no (0.6%) effect on estimated standard deviation.
sum $ map (+ 1) $ map (* 2) $ enumFromTo 1 9001/gotenks
5 5.05 5.10 5.15 5.2 5.25
mean |
10 15 20 25 30 35 40 45 5×10³ iters 100 150 200 250 0 s 50 ms
|
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | 5.06 μs | 5.07 μs | 5.09 μs |
| R² goodness-of-fit | 1.000 | 1.000 | 1.000 |
| Mean execution time | 5.06 μs | 5.08 μs | 5.09 μs |
| Standard deviation | 39.4 ns | 48.8 ns | 64.4 ns |
Outlying measurements have slight (5.6%) effect on estimated standard deviation.
sum $ map (+ 1) $ map (* 2) $ enumFromTo 1 9001/conduit-combinators unqualified
1.05 1.1 1.15 1.2 1.25 1.3 1.35 1.4 1.45 1.5
mean |
100 150 200 50 iters 100 150 200 250 300 0 s 50 ms
|
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | 1.16 ms | 1.24 ms | 1.32 ms |
| R² goodness-of-fit | 0.973 | 0.981 | 0.995 |
| Mean execution time | 1.12 ms | 1.14 ms | 1.17 ms |
| Standard deviation | 53.4 μs | 85.3 μs | 128 μs |
Outlying measurements have severe (58.8%) effect on estimated standard deviation.
sum $ map (+ 1) $ map (* 2) $ enumFromTo 1 9001/conduit-combinators qualified
5.00 5.20 5.4 5.60 5.80 6.00
mean |
10 15 20 25 30 35 40 45 5×10³ iters 100 150 200 250 0 s 50 ms
|
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | 5.06 μs | 5.07 μs | 5.08 μs |
| R² goodness-of-fit | 0.999 | 1.000 | 1.000 |
| Mean execution time | 5.07 μs | 5.10 μs | 5.20 μs |
| Standard deviation | 56.5 ns | 162 ns | 305 ns |
Outlying measurements have moderate (39.6%) effect on estimated standard deviation.
sum $ map (+ 1) $ map (* 2) $ enumFromTo 1 9001/conduit
5.00 5.20 5.4 5.60 5.80
mean |
10 15 20 25 30 35 40 45 5×10³ iters 100 150 200 250 0 s 50 ms
|
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | 5.05 μs | 5.08 μs | 5.12 μs |
| R² goodness-of-fit | 0.998 | 0.999 | 1.000 |
| Mean execution time | 5.06 μs | 5.08 μs | 5.15 μs |
| Standard deviation | 35.1 ns | 120 ns | 246 ns |
Outlying measurements have moderate (26.9%) effect on estimated standard deviation.
sum $ map (+ 1) $ map (* 2) $ enumFromTo 1 9001/vector boxed
5 5.05 5.10 5.15 5.2 5.25 5.3 5.35
mean |
10 15 20 25 30 35 40 45 5×10³ iters 100 150 200 250 0 s 50 ms
|
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | 5.05 μs | 5.06 μs | 5.07 μs |
| R² goodness-of-fit | 1.000 | 1.000 | 1.000 |
| Mean execution time | 5.06 μs | 5.07 μs | 5.09 μs |
| Standard deviation | 31.9 ns | 53.1 ns | 86.3 ns |
Outlying measurements have slight (6.9%) effect on estimated standard deviation.
sum $ map (+ 1) $ map (* 2) $ enumFromTo 1 9001/vector unboxed
5.00 5.20 5.4 5.60 5.80 6.00
mean |
10 15 20 25 30 35 40 45 5×10³ iters 100 150 200 250 0 s 50 ms
|
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | 5.07 μs | 5.11 μs | 5.16 μs |
| R² goodness-of-fit | 0.998 | 0.999 | 1.000 |
| Mean execution time | 5.08 μs | 5.10 μs | 5.20 μs |
| Standard deviation | 51.9 ns | 141 ns | 299 ns |
Outlying measurements have moderate (32.9%) effect on estimated standard deviation.
sum $ map (+ 1) $ map (* 2) $ enumFromTo 1 9001/vector unboxed foldM
5 5.05 5.10 5.15 5.2 5.25
mean |
10 15 20 25 30 35 40 45 5×10³ iters 100 150 200 250 0 s 50 ms
|
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | 5.05 μs | 5.07 μs | 5.09 μs |
| R² goodness-of-fit | 1.000 | 1.000 | 1.000 |
| Mean execution time | 5.06 μs | 5.07 μs | 5.09 μs |
| Standard deviation | 43.9 ns | 55.8 ns | 75.1 ns |
Outlying measurements have slight (7.4%) effect on estimated standard deviation.
sum $ map (+ 1) $ map (* 2) $ enumFromTo 1 9001/direct implementation
5.9 6.00 6.10 6.20 6.30
mean |
10 15 20 25 30 35 5×10³ iters 100 150 200 250 0 s 50 ms
|
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | 5.98 μs | 6.02 μs | 6.08 μs |
| R² goodness-of-fit | 0.999 | 1.000 | 1.000 |
| Mean execution time | 5.98 μs | 6.00 μs | 6.04 μs |
| Standard deviation | 59.8 ns | 82.2 ns | 123 ns |
Outlying measurements have moderate (10.9%) 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.