criterion performance measurements
overview
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Right-assoc/Fused/specialized
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 3.865094537951795e-8 | 3.9258149925106275e-8 | 3.97719887315699e-8 |
| Standard deviation | 1.5169926291285159e-9 | 1.8640250534422384e-9 | 2.374394299320345e-9 |
Outlying measurements have severe (0.6995836622904829%) effect on estimated standard deviation.
Right-assoc/MTL/specialized
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 3.700584265285801e-8 | 3.741950874180447e-8 | 3.7915818472755487e-8 |
| Standard deviation | 1.12954094420348e-9 | 1.4975428587444963e-9 | 2.0152858460225784e-9 |
Outlying measurements have severe (0.6237602046952198%) effect on estimated standard deviation.
Right-assoc/HEffects/generic
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 3.8178655387141444e-8 | 3.8690220797771644e-8 | 3.914936287951023e-8 |
| Standard deviation | 1.322895335315993e-9 | 1.5737546112244717e-9 | 1.874143909937399e-9 |
Outlying measurements have severe (0.6298010844287966%) effect on estimated standard deviation.
Right-assoc/Effects/generic
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 3.5630707016989455e-6 | 3.5951951057409842e-6 | 3.6257385172777753e-6 |
| Standard deviation | 8.67341836647238e-8 | 1.0478289277040958e-7 | 1.2462580502824347e-7 |
Outlying measurements have moderate (0.3641431289693715%) effect on estimated standard deviation.
Left-assoc/Fused/specialized
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 2.0322776112639852e-7 | 2.0663772285859783e-7 | 2.1153783305568493e-7 |
| Standard deviation | 9.735954829312615e-9 | 1.3028132006310816e-8 | 2.0903732467168926e-8 |
Outlying measurements have severe (0.7840346763812172%) effect on estimated standard deviation.
Left-assoc/MTL/specialized
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 1.280583424550477e-7 | 1.304323667301172e-7 | 1.3544899862001925e-7 |
| Standard deviation | 6.7684807966987065e-9 | 1.150202258869754e-8 | 2.0376496378991134e-8 |
Outlying measurements have severe (0.8830468170484747%) effect on estimated standard deviation.
Left-assoc/HEffects/generic
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 2.1859188487110195e-7 | 2.226948737424645e-7 | 2.2762281210288854e-7 |
| Standard deviation | 1.2372038106465621e-8 | 1.652947144190077e-8 | 2.318164053270907e-8 |
Outlying measurements have severe (0.8346472822884521%) effect on estimated standard deviation.
Left-assoc/Effects/generic
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 3.721911754035327e-6 | 3.7684087045812933e-6 | 3.8342105050848794e-6 |
| Standard deviation | 1.4091623803813883e-7 | 1.9513365176646858e-7 | 2.760259460491142e-7 |
Outlying measurements have severe (0.6491203164394679%) 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.