criterion performance measurements
overview
want to understand this report?
encode/encodeSimpleRaw
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 5.180641621846145e-7 | 5.220398006277814e-7 | 5.268816539321792e-7 |
| Standard deviation | 1.254116341943779e-8 | 1.5083517585868545e-8 | 1.8000030978979116e-8 |
Outlying measurements have moderate (0.4049287592681779%) effect on estimated standard deviation.
encode/encodeSimplePoke
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 5.142424837497961e-7 | 5.18300519376917e-7 | 5.217618674290082e-7 |
| Standard deviation | 1.0809586350520384e-8 | 1.2410329558246944e-8 | 1.4583235110446392e-8 |
Outlying measurements have moderate (0.3198749883141072%) effect on estimated standard deviation.
encode/encodeSimplePokeMonad
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 5.170458592562339e-7 | 5.208577187287666e-7 | 5.257831625188327e-7 |
| Standard deviation | 1.159248357739297e-8 | 1.4799669174682987e-8 | 2.0491494257563075e-8 |
Outlying measurements have moderate (0.3986382327583577%) effect on estimated standard deviation.
encode/encodeSimplePokeRef
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 8.011684350972629e-7 | 8.071560535819634e-7 | 8.161263240732273e-7 |
| Standard deviation | 1.80670831727965e-8 | 2.5368903904649243e-8 | 4.007373077242936e-8 |
Outlying measurements have moderate (0.4386299111663943%) effect on estimated standard deviation.
encode/encodeSimplePokeRefMonad
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 8.070071836689238e-7 | 8.137418531535078e-7 | 8.221666235753555e-7 |
| Standard deviation | 1.9365241545405652e-8 | 2.419644822005777e-8 | 3.0153364139682035e-8 |
Outlying measurements have moderate (0.4110835135820688%) effect on estimated standard deviation.
encode/encodeBuilderLE
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 3.0550295602101125e-6 | 3.07843265897453e-6 | 3.1052007564153563e-6 |
| Standard deviation | 6.177046985891318e-8 | 8.114910329663927e-8 | 1.0022502575363955e-7 |
Outlying measurements have moderate (0.3216750399054145%) effect on estimated standard deviation.
encode/encodeBuilderBE
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 3.4946410414104074e-6 | 3.517255383300536e-6 | 3.5475236084652265e-6 |
| Standard deviation | 6.656957742145082e-8 | 8.77012882488578e-8 | 1.195999439232658e-7 |
Outlying measurements have moderate (0.2952122450524315%) effect on estimated standard deviation.
encode/encodeCereal
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 1.7867321247777237e-5 | 1.8258624768395036e-5 | 1.9793448952657418e-5 |
| Standard deviation | 6.61670846418653e-7 | 2.1809767563929635e-6 | 4.757870487746159e-6 |
Outlying measurements have severe (0.8954629791568455%) effect on estimated standard deviation.
encode/binary
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 7.178682246372097e-5 | 7.363863698288497e-5 | 7.712270313811774e-5 |
| Standard deviation | 5.285703267607544e-6 | 8.301237718982114e-6 | 1.3026215928073665e-5 |
Outlying measurements have severe (0.8543645454028248%) effect on estimated standard deviation.
decode/decodeSimplePeek
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 1.0906457395753334e-6 | 1.133101896538444e-6 | 1.2036845211237992e-6 |
| Standard deviation | 1.2780952338903884e-7 | 1.772418344574898e-7 | 2.4525017404943686e-7 |
Outlying measurements have severe (0.9541398060714832%) effect on estimated standard deviation.
decode/decodeSimplePeekEx
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 1.8365638590373317e-6 | 1.9321970429665704e-6 | 2.0715685418720894e-6 |
| Standard deviation | 2.587294591147157e-7 | 3.699617411004546e-7 | 5.763399272193097e-7 |
Outlying measurements have severe (0.9676729435916178%) effect on estimated standard deviation.
decode/decodeRawLE
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 1.936399683804126e-6 | 1.9958539146327316e-6 | 2.1053122661797122e-6 |
| Standard deviation | 1.7835396980421424e-7 | 2.543558462917517e-7 | 4.155163448624974e-7 |
Outlying measurements have severe (0.9247199805803751%) effect on estimated standard deviation.
decode/decodeRawBE
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 2.9520832746653492e-6 | 3.0807903465831463e-6 | 3.26359519952505e-6 |
| Standard deviation | 3.5941543955309224e-7 | 5.387742755063893e-7 | 7.608901868261365e-7 |
Outlying measurements have severe (0.9604563459910329%) effect on estimated standard deviation.
decode/decodeCereal
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 9.64215103055128e-6 | 9.929397521093321e-6 | 1.0254319788409688e-5 |
| Standard deviation | 8.647213815279294e-7 | 1.0213832955339816e-6 | 1.160364196748537e-6 |
Outlying measurements have severe (0.8711068260281312%) effect on estimated standard deviation.
decode/binary
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 7.841369649058291e-5 | 8.042479976381317e-5 | 8.318473989384546e-5 |
| Standard deviation | 6.073290325864003e-6 | 7.678440226311237e-6 | 1.0331321098803156e-5 |
Outlying measurements have severe (0.8086983265514696%) 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.