criterion performance measurements

overview

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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.

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.

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.