criterion performance measurements
- -overview
- -want to understand this report?
- - - -fib/1
-| - | - - |
| - | lower bound | -estimate | -upper bound | - - -
|---|---|---|---|
| OLS regression | -xxx | -xxx | -xxx | -
| R² goodness-of-fit | -xxx | -xxx | -xxx | -
| Mean execution time | -2.31459993168433e-8 | -2.374225969306158e-8 | -2.4336041431094957e-8 | -
| Standard deviation | -1.7147402747620926e-9 | -1.984234308811127e-9 | -2.3435359738948246e-9 | -
Outlying measurements have severe - (0.8827515417826841%) - effect on estimated standard deviation.
- -fib/5
-| - | - - |
| - | lower bound | -estimate | -upper bound | - - -
|---|---|---|---|
| OLS regression | -xxx | -xxx | -xxx | -
| R² goodness-of-fit | -xxx | -xxx | -xxx | -
| Mean execution time | -3.640686812141915e-7 | -3.7647973827317373e-7 | -3.8862828356384757e-7 | -
| Standard deviation | -3.5904833037515274e-8 | -4.150785932735141e-8 | -4.81505001531474e-8 | -
Outlying measurements have severe - (0.917699613099007%) - effect on estimated standard deviation.
- -fib/9
-| - | - - |
| - | lower bound | -estimate | -upper bound | - - -
|---|---|---|---|
| OLS regression | -xxx | -xxx | -xxx | -
| R² goodness-of-fit | -xxx | -xxx | -xxx | -
| Mean execution time | -2.5489390737084626e-6 | -2.614524699113428e-6 | -2.700766045605913e-6 | -
| Standard deviation | -2.0893167057513842e-7 | -2.4922772413717383e-7 | -3.0480780278156827e-7 | -
Outlying measurements have severe - (0.86814310186276%) - effect on estimated standard deviation.
- -fib/11
-| - | - - |
| - | lower bound | -estimate | -upper bound | - - -
|---|---|---|---|
| OLS regression | -xxx | -xxx | -xxx | -
| R² goodness-of-fit | -xxx | -xxx | -xxx | -
| Mean execution time | -6.347714383730146e-6 | -6.496202868182492e-6 | -6.668634037917654e-6 | -
| Standard deviation | -4.0420784296930194e-7 | -4.919233380857326e-7 | -6.202125623223447e-7 | -
Outlying measurements have severe - (0.7876656352417168%) - 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.
- --
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- 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.
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- 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.
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