criterion performance measurements
overview
want to understand this report?
L'Ecuyer
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 8.59826300453143e-4 | 8.763282077112986e-4 | 8.981517426871305e-4 |
| Standard deviation | 5.380919281680885e-5 | 6.59275818265822e-5 | 8.882591697695772e-5 |
Outlying measurements have severe (0.6072057860609948%) effect on estimated standard deviation.
MT/Pure64
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 1.0479229179194505e-3 | 1.0668436428914888e-3 | 1.0913634231687007e-3 |
| Standard deviation | 6.0924120496761496e-5 | 7.362573009402315e-5 | 9.198804438604227e-5 |
Outlying measurements have severe (0.5561251616606299%) effect on estimated standard deviation.
MT/IO
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 1.8700612119286926e-5 | 1.902253797488705e-5 | 1.9363836173401936e-5 |
| Standard deviation | 9.518365085606007e-7 | 1.1067303592158323e-6 | 1.324953797490343e-6 |
Outlying measurements have severe (0.6551973498827334%) effect on estimated standard deviation.
MWC
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 2.4141310791790776e-5 | 2.4526654295893692e-5 | 2.5052506021827708e-5 |
| Standard deviation | 1.1783287810601005e-6 | 1.5115523864321788e-6 | 2.36369759489426e-6 |
Outlying measurements have severe (0.6782963352223086%) effect on estimated standard deviation.
tf
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 9.01366550408456e-4 | 9.174389880421074e-4 | 9.37021750082472e-4 |
| Standard deviation | 4.702974736149671e-5 | 5.5936550951585726e-5 | 7.373976395543261e-5 |
Outlying measurements have moderate (0.4984722748147162%) effect on estimated standard deviation.
xorshift/32bit
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 9.62315909302769e-4 | 9.795307678306263e-4 | 9.967326949544032e-4 |
| Standard deviation | 5.013619017168908e-5 | 5.9507397914500704e-5 | 7.293133502548568e-5 |
Outlying measurements have moderate (0.4917121804457075%) effect on estimated standard deviation.
xorshift/64bit
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 9.722304139861127e-4 | 9.924154451956388e-4 | 1.0212004833844732e-3 |
| Standard deviation | 6.165215626777716e-5 | 8.468129453337734e-5 | 1.2348858394797818e-4 |
Outlying measurements have severe (0.6622417000969626%) effect on estimated standard deviation.
baseline
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 1.0710809272554955e-5 | 1.0887460809241469e-5 | 1.1126496730710819e-5 |
| Standard deviation | 5.348086511576579e-7 | 6.948870330730048e-7 | 9.958247998915603e-7 |
Outlying measurements have severe (0.7098720930466001%) 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.