criterion performance measurements
overview
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8/alloca
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 9.754965521064537e-9 | 9.933553315582286e-9 | 1.014044120518682e-8 |
| Standard deviation | 5.202914292347851e-10 | 6.34347305379142e-10 | 7.634998188056154e-10 |
Outlying measurements have severe (0.8246379013402185%) effect on estimated standard deviation.
8/malloc
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 9.287296594786745e-8 | 9.447825058436855e-8 | 9.633131652050455e-8 |
| Standard deviation | 4.855756312461947e-9 | 6.019528759010579e-9 | 7.90817988125518e-9 |
Outlying measurements have severe (0.7980345285349946%) effect on estimated standard deviation.
128/alloca
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 1.507130803702627e-8 | 1.5332374736181416e-8 | 1.5653754383164132e-8 |
| Standard deviation | 7.692201911328508e-10 | 9.60817056169962e-10 | 1.215231395140716e-9 |
Outlying measurements have severe (0.8157994896332994%) effect on estimated standard deviation.
128/malloc
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 9.258463306799614e-8 | 9.392300061652206e-8 | 9.547221716100846e-8 |
| Standard deviation | 4.075588698483175e-9 | 5.056272265437833e-9 | 6.288572298350279e-9 |
Outlying measurements have severe (0.738335873536639%) effect on estimated standard deviation.
2048/alloca
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 6.99834235330523e-8 | 7.103513081303001e-8 | 7.210311547741869e-8 |
| Standard deviation | 3.0037512110314825e-9 | 3.5079071201186985e-9 | 4.281120561397185e-9 |
Outlying measurements have severe (0.7081944365473222%) effect on estimated standard deviation.
2048/malloc
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 8.813692100730395e-8 | 8.970891264131536e-8 | 9.235057265625165e-8 |
| Standard deviation | 3.925533472475387e-9 | 6.026253404471314e-9 | 1.0726377722714802e-8 |
Outlying measurements have severe (0.814752523519701%) effect on estimated standard deviation.
32768/alloca
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 2.533012492662019e-7 | 2.56716905081981e-7 | 2.603647618255581e-7 |
| Standard deviation | 1.035203648226192e-8 | 1.2442763075332649e-8 | 1.533694842643189e-8 |
Outlying measurements have severe (0.6771127226095579%) effect on estimated standard deviation.
32768/malloc
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 8.946033747513347e-8 | 9.103338404562111e-8 | 9.273731341211018e-8 |
| Standard deviation | 4.457573354644581e-9 | 5.447314429943905e-9 | 6.890152589473899e-9 |
Outlying measurements have severe (0.7780276980393149%) effect on estimated standard deviation.
131072/alloca
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 8.350412634658868e-7 | 8.48249753172544e-7 | 8.631320964188266e-7 |
| Standard deviation | 3.94496834734956e-8 | 4.70527158454293e-8 | 5.7075307929537935e-8 |
Outlying measurements have severe (0.71314679201857%) effect on estimated standard deviation.
131072/malloc
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 4.2501415735582563e-7 | 4.3011785415611764e-7 | 4.3797197355259694e-7 |
| Standard deviation | 1.6783039075434175e-8 | 2.0600590918728475e-8 | 2.7061591704333416e-8 |
Outlying measurements have severe (0.6632291366696168%) 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.