criterion performance measurements
overview
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collatzATS/2223
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 1.6693566415934183e-7 | 1.675119130296263e-7 | 1.6830855924199545e-7 |
| Standard deviation | 1.649463607145627e-9 | 2.218206353974801e-9 | 3.1706280849944007e-9 |
Outlying measurements have moderate (0.13445683318348203%) effect on estimated standard deviation.
collatzATS/10971
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 2.418110160217941e-7 | 2.422842337598726e-7 | 2.430189363264822e-7 |
| Standard deviation | 1.2685785059373994e-9 | 1.8865772607857675e-9 | 2.441589725168413e-9 |
Outlying measurements have no (4.14930555555573e-3%) effect on estimated standard deviation.
collatzATS/106239
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 3.323910520624374e-7 | 3.328319212151914e-7 | 3.334343695526644e-7 |
| Standard deviation | 1.2239724417113533e-9 | 1.6718211195876744e-9 | 2.3280000523858638e-9 |
Outlying measurements have no (4.273425555821427e-3%) effect on estimated standard deviation.
collatzC/2223
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 2.012154419174298e-7 | 2.01353176090842e-7 | 2.0147342328732802e-7 |
| Standard deviation | 3.565288334994199e-10 | 4.3265186286717216e-10 | 5.486757030335035e-10 |
Outlying measurements have no (4.098291249640202e-3%) effect on estimated standard deviation.
collatzC/10971
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 2.9627722380850305e-7 | 2.9654214740301673e-7 | 2.968696746357324e-7 |
| Standard deviation | 8.113017952808612e-10 | 1.0127048204950202e-9 | 1.2907430998930116e-9 |
Outlying measurements have no (4.219333524849193e-3%) effect on estimated standard deviation.
collatzC/106239
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 3.9167881376023823e-7 | 3.919369585793171e-7 | 3.9216328073637717e-7 |
| Standard deviation | 7.018986308159933e-10 | 8.589640970091144e-10 | 1.0732250375229076e-9 |
Outlying measurements have no (4.32892249527418e-3%) effect on estimated standard deviation.
factorial/factorial 50
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 9.801224209295593e-8 | 9.828551539750831e-8 | 9.852378652690458e-8 |
| Standard deviation | 6.824053453141056e-10 | 8.680439112717062e-10 | 1.226716968483525e-9 |
Outlying measurements have slight (6.943261914982943e-2%) effect on estimated standard deviation.
factorial/factorialATS 50
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 1.003012301158519e-6 | 1.005290323028677e-6 | 1.0079470916276459e-6 |
| Standard deviation | 6.942890269594465e-9 | 8.334950969105665e-9 | 1.0532476452673302e-8 |
Outlying measurements have no (4.716875182498106e-3%) effect on estimated standard deviation.
fibonacci/fibonacci (50)
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 9.835421637395329e-8 | 9.862426209021127e-8 | 9.894567546365841e-8 |
| Standard deviation | 7.097434989352116e-10 | 9.269129458562889e-10 | 1.2700548396253749e-9 |
Outlying measurements have slight (7.685290950609494e-2%) effect on estimated standard deviation.
fibonacci/fibonacciATS (50)
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 5.049016266866323e-7 | 5.068544549653444e-7 | 5.105728744430492e-7 |
| Standard deviation | 5.672658100206097e-9 | 8.582389465504354e-9 | 1.3362649673840823e-8 |
Outlying measurements have moderate (0.19031169956576133%) effect on estimated standard deviation.
fibonacci/fibonacciGMP (50)
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 8.738981449283216e-8 | 8.77077636592795e-8 | 8.81092889259842e-8 |
| Standard deviation | 8.816736077368508e-10 | 1.2112069338307398e-9 | 1.9217182959246496e-9 |
Outlying measurements have moderate (0.15331531666794954%) effect on estimated standard deviation.
derangement/derangement (64)
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 1.2023521338551608e-7 | 1.20599930779622e-7 | 1.2107997033009853e-7 |
| Standard deviation | 1.08081343137005e-9 | 1.400430182287631e-9 | 1.9906291419274604e-9 |
Outlying measurements have moderate (0.1117462009440993%) effect on estimated standard deviation.
derangement/id (96800425246141091510518408809597121)
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 8.194830449924301e-9 | 8.384674898817535e-9 | 8.882700962740839e-9 |
| Standard deviation | 3.783843842188408e-10 | 9.280647429086103e-10 | 1.7736430537591763e-9 |
Outlying measurements have severe (0.9348758909597994%) effect on estimated standard deviation.
derangement/derangementATS (64)
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 1.774066154216098e-6 | 1.7943597709786685e-6 | 1.8490852639709685e-6 |
| Standard deviation | 4.4345378191785345e-8 | 1.0200000814632702e-7 | 1.9410432825967158e-7 |
Outlying measurements have severe (0.7060456294384498%) 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.