criterion performance measurements
overview
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bench/./Curry/Strings 5
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 2.456638057571667e-2 | 2.4807460438799597e-2 | 2.4984550376137424e-2 |
| Standard deviation | 2.9086308890971996e-4 | 4.458710200566661e-4 | 6.651375641405684e-4 |
Outlying measurements have slight (4.986149584487535e-2%) effect on estimated standard deviation.
bench/./Curry/Strings 6
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 2.5048034973156462e-2 | 2.5507298777456923e-2 | 2.6156068292579472e-2 |
| Standard deviation | 8.561992995742435e-4 | 1.2009956091337942e-3 | 1.7221986162968255e-3 |
Outlying measurements have moderate (0.14864673661167627%) effect on estimated standard deviation.
bench/./Curry/Strings 7
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 2.480268860429386e-2 | 2.5154580387234622e-2 | 2.550788242709118e-2 |
| Standard deviation | 5.31814352372895e-4 | 7.262286441793511e-4 | 9.765343198335565e-4 |
Outlying measurements have slight (6.186215538128722e-2%) effect on estimated standard deviation.
bench/./Curry/Strings 8
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 2.466611037790698e-2 | 2.4910298818876016e-2 | 2.5075635970087558e-2 |
| Standard deviation | 3.176062030700934e-4 | 4.5304021229476796e-4 | 6.228306630958208e-4 |
Outlying measurements have slight (4.986149584487534e-2%) effect on estimated standard deviation.
bench/./Curry/Strings 9
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 2.455496950266148e-2 | 2.4886785650988636e-2 | 2.5400436058391762e-2 |
| Standard deviation | 5.997921688939632e-4 | 9.101060311759392e-4 | 1.3801989406811042e-3 |
Outlying measurements have slight (9.844961735627182e-2%) effect on estimated standard deviation.
bench/./Curry/Strings 10
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 2.666520833361659e-2 | 2.8325515819218643e-2 | 3.143958975996814e-2 |
| Standard deviation | 2.7790848798340036e-3 | 4.841386515167383e-3 | 7.324829420066575e-3 |
Outlying measurements have severe (0.7067231622942967%) effect on estimated standard deviation.
bench/python ProbLog/strings.py 5
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 0.29585640316363426 | 0.307945092311129 | 0.3208043945580721 |
| Standard deviation | 1.246956675163447e-2 | 1.6272688072700048e-2 | 1.9447803215874686e-2 |
Outlying measurements have moderate (0.16000000000000003%) effect on estimated standard deviation.
bench/python ProbLog/strings.py 6
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 0.5808082894654945 | 0.6051154828213233 | 0.6294226761771522 |
| Standard deviation | 2.2065463165442167e-2 | 2.8678898240206953e-2 | 3.3358326310985086e-2 |
Outlying measurements have moderate (0.18749999999999997%) effect on estimated standard deviation.
bench/python ProbLog/strings.py 7
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 1.3951783885713667 | 1.458959262847202 | 1.4929082564194687 |
| Standard deviation | 9.508064892108176e-3 | 6.075824527169972e-2 | 7.840184231066039e-2 |
Outlying measurements have moderate (0.1875%) effect on estimated standard deviation.
bench/python ProbLog/strings.py 8
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 4.689921961107757 | 4.785397633745258 | 4.964600045525003 |
| Standard deviation | 8.050317119341344e-3 | 0.17575870414949152 | 0.21157297411684656 |
Outlying measurements have moderate (0.18749999999999997%) effect on estimated standard deviation.
bench/python ProbLog/strings.py 9
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 14.747978235852012 | 14.766109396233029 | 14.785484165651724 |
| Standard deviation | 1.1645961591663756e-2 | 2.2765072905658757e-2 | 3.129651523764266e-2 |
Outlying measurements have moderate (0.1875%) effect on estimated standard deviation.
bench/python ProbLog/strings.py 10
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 60.86092587552654 | 62.35398975452214 | 63.48802126732577 |
| Standard deviation | 0.6675672191847323 | 1.5276135072326322 | 2.0223389755228145 |
Outlying measurements have moderate (0.1875%) effect on estimated standard deviation.
bench/./WebPPL/node_modules/.bin/webppl WebPPL/strings.wppl 5
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 1.56350901623955 | 1.7269433498149738 | 1.862776642316021 |
| Standard deviation | 0.11709924246055987 | 0.1888403745365269 | 0.23037632596665447 |
Outlying measurements have moderate (0.2295823136310635%) effect on estimated standard deviation.
bench/./WebPPL/node_modules/.bin/webppl WebPPL/strings.wppl 6
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 1.6732987511980657 | 1.7585320380652167 | 1.802752246808571 |
| Standard deviation | 7.40655090611142e-3 | 8.070860066370852e-2 | 0.1021221976863882 |
Outlying measurements have moderate (0.18749999999999994%) effect on estimated standard deviation.
bench/./WebPPL/node_modules/.bin/webppl WebPPL/strings.wppl 7
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 1.7799352763977367 | 1.8827166481156987 | 2.0475579957128502 |
| Standard deviation | 1.4529622687874444e-2 | 0.16574566541231617 | 0.210859272946412 |
Outlying measurements have moderate (0.21849890729501006%) effect on estimated standard deviation.
bench/./WebPPL/node_modules/.bin/webppl WebPPL/strings.wppl 8
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 1.5973343982477672 | 1.752790626002631 | 1.8527464295038953 |
| Standard deviation | 4.968112058705487e-2 | 0.16043964923990023 | 0.19661627333332618 |
Outlying measurements have moderate (0.22086086012196826%) effect on estimated standard deviation.
bench/./WebPPL/node_modules/.bin/webppl WebPPL/strings.wppl 9
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 1.5769065973581746 | 1.588657249492826 | 1.6064260390412528 |
| Standard deviation | 4.298535524867475e-3 | 1.6916515564024993e-2 | 2.2173447407571545e-2 |
Outlying measurements have moderate (0.1875%) effect on estimated standard deviation.
bench/./WebPPL/node_modules/.bin/webppl WebPPL/strings.wppl 10
| lower bound | estimate | upper bound | |
|---|---|---|---|
| OLS regression | xxx | xxx | xxx |
| R² goodness-of-fit | xxx | xxx | xxx |
| Mean execution time | 1.6309773861285066 | 1.69718680002552 | 1.7622161235194653 |
| Standard deviation | 4.3436929689233655e-2 | 8.115118165998828e-2 | 9.817051071727279e-2 |
Outlying measurements have moderate (0.1875%) 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.