criterion performance measurements
overview
want to understand this report?
fromList/HashMap.Strict
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 5.823798239025967e-2 | 5.9465569626123836e-2 | 6.0694053281797784e-2 |
| Standard deviation | 1.6944658578048838e-3 | 2.112252319987771e-3 | 2.7417215018326795e-3 |
Outlying measurements have slight (7.638888888888887e-2%) effect on estimated standard deviation.
fromList/LinkedHashMap.Seq
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 0.10609368783234072 | 0.10741278217800422 | 0.10878098875918617 |
| Standard deviation | 1.470629283793434e-3 | 2.177175578188104e-3 | 3.1821417017396407e-3 |
Outlying measurements have slight (9.876543209876543e-2%) effect on estimated standard deviation.
fromList/LinkedHashMap.IntMap
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 0.1419488462850627 | 0.14398421525034588 | 0.14570120007874124 |
| Standard deviation | 1.6606168785365059e-3 | 2.46836135133423e-3 | 3.8754143338653735e-3 |
Outlying measurements have moderate (0.12244897959183672%) effect on estimated standard deviation.
fromList/LinkedHashSet
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 0.11078549811760263 | 0.11302955501645821 | 0.11555986785536974 |
| Standard deviation | 2.4100957998699573e-3 | 3.5384903020810893e-3 | 5.039968462750431e-3 |
Outlying measurements have moderate (0.10937500000000001%) effect on estimated standard deviation.
insert/HashMap.Strict
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 9.66336663500253e-2 | 9.805565911400033e-2 | 0.10169472698842674 |
| Standard deviation | 1.1675552235736965e-3 | 3.484700388495005e-3 | 5.382121244429772e-3 |
Outlying measurements have slight (9.876543209876543e-2%) effect on estimated standard deviation.
insert/LinkedHashMap.Seq
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 0.2025353848814711 | 0.20345971933705315 | 0.2040105427529429 |
| Standard deviation | 5.210753868301209e-4 | 9.459630195315161e-4 | 1.336499212402788e-3 |
Outlying measurements have moderate (0.13888888888888867%) effect on estimated standard deviation.
insert/LinkedHashMap.IntMap
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 0.1450154283838266 | 0.14934753241199314 | 0.15289369478044953 |
| Standard deviation | 2.923149843639434e-3 | 4.974364358895773e-3 | 7.327519016203888e-3 |
Outlying measurements have moderate (0.12244897959183673%) effect on estimated standard deviation.
insert/LinkedHashSet
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 0.17667107898713286 | 0.18105530125365907 | 0.18455605071288272 |
| Standard deviation | 3.797947273162578e-3 | 5.192148211293511e-3 | 6.460445519273326e-3 |
Outlying measurements have moderate (0.13888888888888876%) effect on estimated standard deviation.
toList/HashMap.Strict
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 1.0986631386483989e-2 | 1.1313557825852036e-2 | 1.164065580285678e-2 |
| Standard deviation | 6.512792102167961e-4 | 8.981265209645694e-4 | 1.3803696126366038e-3 |
Outlying measurements have moderate (0.39915408748273007%) effect on estimated standard deviation.
toList/LinkedHashMap.Seq
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 7.316210535862285e-3 | 7.712936779412751e-3 | 8.352414291846506e-3 |
| Standard deviation | 8.878397022384322e-4 | 1.3654877505479197e-3 | 2.224516357372639e-3 |
Outlying measurements have severe (0.8135853625809822%) effect on estimated standard deviation.
toList/LinkedHashMap.IntMap
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 5.457266993651209e-3 | 5.8983559099956145e-3 | 6.548090342030358e-3 |
| Standard deviation | 1.1952569119041787e-3 | 1.63354360340212e-3 | 2.493605304861085e-3 |
Outlying measurements have severe (0.9186261885277799%) effect on estimated standard deviation.
toList/LinkedHashSet
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 7.833669556858998e-3 | 8.499252838938794e-3 | 9.521341117716452e-3 |
| Standard deviation | 1.7282944199500537e-3 | 2.3990190362993507e-3 | 3.7117266247703473e-3 |
Outlying measurements have severe (0.9341495406611672%) effect on estimated standard deviation.
lookup/HashMap.Strict
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 2.489899337265906e-2 | 2.5099465869095842e-2 | 2.5270911792373504e-2 |
| Standard deviation | 2.5311471539792393e-4 | 4.1923359279769426e-4 | 6.362718379228563e-4 |
Outlying measurements have slight (4.986149584487534e-2%) effect on estimated standard deviation.
lookup/LinkedHashMap.Seq
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 3.131436702754379e-2 | 3.1670726626938255e-2 | 3.1926745996675436e-2 |
| Standard deviation | 4.7097470024281657e-4 | 6.365485988152492e-4 | 8.215187597577873e-4 |
Outlying measurements have slight (5.536332179930796e-2%) effect on estimated standard deviation.
lookup/LinkedHashMap.IntMap
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 3.296754303406223e-2 | 3.324609867908928e-2 | 3.345123926528852e-2 |
| Standard deviation | 3.4113036713684933e-4 | 4.982019145900714e-4 | 7.059837291481826e-4 |
Outlying measurements have slight (5.859375e-2%) effect on estimated standard deviation.
lookup/LinkedHashSet
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 1.774921111186316e-2 | 1.7907792508456342e-2 | 1.808260369159567e-2 |
| Standard deviation | 3.7003447632050164e-4 | 4.3620364011790386e-4 | 5.389477858021622e-4 |
Outlying measurements have slight (4.158790170132314e-2%) effect on estimated standard deviation.
delete/HashMap.Strict
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 6.564812154217825e-2 | 6.996261943265925e-2 | 7.454172138274426e-2 |
| Standard deviation | 4.813357130653396e-3 | 7.819464531132397e-3 | 1.2066551604678682e-2 |
Outlying measurements have moderate (0.3486000959853333%) effect on estimated standard deviation.
delete/LinkedHashMap.Seq
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 0.31174286131219 | 0.32376926768186787 | 0.33100393725365035 |
| Standard deviation | 5.566456097511906e-3 | 1.202011762004601e-2 | 1.6707753699066505e-2 |
Outlying measurements have moderate (0.16%) effect on estimated standard deviation.
delete/LinkedHashMap.IntMap
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 0.3032040686980184 | 0.3120361126816615 | 0.32156761120577176 |
| Standard deviation | 4.8044378714013625e-3 | 1.1486830727845178e-2 | 1.4849044889087004e-2 |
Outlying measurements have moderate (0.16%) effect on estimated standard deviation.
delete/LinkedHashSet
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 0.32236446230697285 | 0.3298588115774167 | 0.3340062524635349 |
| Standard deviation | 1.8487506216196886e-3 | 7.187402823015662e-3 | 9.578104448193504e-3 |
Outlying measurements have moderate (0.16%) 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.