criterion performance measurements
overview
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| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 1.1127952052099233e-4 | 1.1157779769569623e-4 | 1.1222578474109467e-4 |
| Standard deviation | 7.006642862776891e-7 | 1.6019470204849632e-6 | 3.0016649862884594e-6 |
Outlying measurements have slight (8.197243287309262e-2%) effect on estimated standard deviation.
client stream: 100 messages
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 1.1145656673217617e-3 | 1.1225264180385309e-3 | 1.136100231001483e-3 |
| Standard deviation | 2.1175077057984874e-5 | 3.660369985889931e-5 | 6.0245576168224164e-5 |
Outlying measurements have moderate (0.2100730004584609%) effect on estimated standard deviation.
client stream: 1k messages
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 1.0274288101958585e-2 | 1.0341389459833119e-2 | 1.0418416003070792e-2 |
| Standard deviation | 1.5974251878414234e-4 | 2.0913496502759373e-4 | 2.889361409397357e-4 |
Outlying measurements have slight (3.222222222222209e-2%) effect on estimated standard deviation.
client stream: 10k messages
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 0.1035139522385206 | 0.1044357553238654 | 0.10589733927787692 |
| Standard deviation | 8.693634002187114e-4 | 1.7581790983451108e-3 | 2.6984798923229657e-3 |
Outlying measurements have slight (9.876543209876533e-2%) effect on estimated standard deviation.
server stream: 100 messages
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 1.00500185533956e-3 | 1.0219297837648412e-3 | 1.038431407924066e-3 |
| Standard deviation | 4.534639216316358e-5 | 5.8708277292223296e-5 | 7.292687430223964e-5 |
Outlying measurements have moderate (0.4697581699045145%) effect on estimated standard deviation.
server stream: 1k messages
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 8.263458511284804e-3 | 8.29655676830788e-3 | 8.338747504141219e-3 |
| Standard deviation | 7.989147957982453e-5 | 1.1338955133128914e-4 | 1.6239809568186118e-4 |
Outlying measurements have slight (2.938475665748384e-2%) effect on estimated standard deviation.
server stream: 10k messages
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 8.139915127469478e-2 | 8.203977915769449e-2 | 8.294207157255142e-2 |
| Standard deviation | 8.712272680628149e-4 | 1.303323554210239e-3 | 1.75288845055683e-3 |
Outlying measurements have slight (9.000000000000001e-2%) effect on estimated standard deviation.
bidi stream: 50 messages up, 50 down
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 3.6837263323658102e-3 | 3.690185989917211e-3 | 3.7016024806948455e-3 |
| Standard deviation | 1.87435230068336e-5 | 2.7406270029965376e-5 | 3.868689618608243e-5 |
Outlying measurements have slight (2.1266540642722116e-2%) effect on estimated standard deviation.
bidi stream: 500 message up, 500 down
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 3.539779838485038e-2 | 3.5467452958929724e-2 | 3.5585300045038584e-2 |
| Standard deviation | 1.1602363892840695e-4 | 1.603301186146598e-4 | 2.1296560804213925e-4 |
Outlying measurements have slight (5.8593749999999986e-2%) effect on estimated standard deviation.
bidi stream: 5000 messages up, 5000 down
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 0.3519412043621479 | 0.35207846750875343 | 0.3521320771337429 |
| Standard deviation | 0.0 | 1.2712910344311448e-4 | 1.4489214967143587e-4 |
Outlying measurements have moderate (0.1875%) 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.