-
criterion performance measurements
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-
overview
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want to understand this report?
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- lower bound |
- estimate |
- upper bound |
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- OLS regression |
- xxx |
- xxx |
- xxx |
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- R² goodness-of-fit |
- xxx |
- xxx |
- xxx |
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- Mean execution time |
- 1.1127952052099233e-4 |
- 1.1157779769569623e-4 |
- 1.1222578474109467e-4 |
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- Standard deviation |
- 7.006642862776891e-7 |
- 1.6019470204849632e-6 |
- 3.0016649862884594e-6 |
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- Outlying measurements have slight
- (8.197243287309262e-2%)
- effect on estimated standard deviation.
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- lower bound |
- estimate |
- upper bound |
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- OLS regression |
- xxx |
- xxx |
- xxx |
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- R² goodness-of-fit |
- xxx |
- xxx |
- xxx |
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- Mean execution time |
- 1.1145656673217617e-3 |
- 1.1225264180385309e-3 |
- 1.136100231001483e-3 |
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- Standard deviation |
- 2.1175077057984874e-5 |
- 3.660369985889931e-5 |
- 6.0245576168224164e-5 |
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- Outlying measurements have moderate
- (0.2100730004584609%)
- effect on estimated standard deviation.
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- lower bound |
- estimate |
- upper bound |
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- OLS regression |
- xxx |
- xxx |
- xxx |
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- R² goodness-of-fit |
- xxx |
- xxx |
- xxx |
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- Mean execution time |
- 1.0274288101958585e-2 |
- 1.0341389459833119e-2 |
- 1.0418416003070792e-2 |
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- Standard deviation |
- 1.5974251878414234e-4 |
- 2.0913496502759373e-4 |
- 2.889361409397357e-4 |
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- Outlying measurements have slight
- (3.222222222222209e-2%)
- effect on estimated standard deviation.
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- lower bound |
- estimate |
- upper bound |
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- OLS regression |
- xxx |
- xxx |
- xxx |
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- R² goodness-of-fit |
- xxx |
- xxx |
- xxx |
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- Mean execution time |
- 0.1035139522385206 |
- 0.1044357553238654 |
- 0.10589733927787692 |
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- Standard deviation |
- 8.693634002187114e-4 |
- 1.7581790983451108e-3 |
- 2.6984798923229657e-3 |
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- Outlying measurements have slight
- (9.876543209876533e-2%)
- effect on estimated standard deviation.
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- lower bound |
- estimate |
- upper bound |
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- OLS regression |
- xxx |
- xxx |
- xxx |
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- R² goodness-of-fit |
- xxx |
- xxx |
- xxx |
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- Mean execution time |
- 1.00500185533956e-3 |
- 1.0219297837648412e-3 |
- 1.038431407924066e-3 |
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- Standard deviation |
- 4.534639216316358e-5 |
- 5.8708277292223296e-5 |
- 7.292687430223964e-5 |
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- Outlying measurements have moderate
- (0.4697581699045145%)
- effect on estimated standard deviation.
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- |
- lower bound |
- estimate |
- upper bound |
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-
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- OLS regression |
- xxx |
- xxx |
- xxx |
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- R² goodness-of-fit |
- xxx |
- xxx |
- xxx |
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- Mean execution time |
- 8.263458511284804e-3 |
- 8.29655676830788e-3 |
- 8.338747504141219e-3 |
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- Standard deviation |
- 7.989147957982453e-5 |
- 1.1338955133128914e-4 |
- 1.6239809568186118e-4 |
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- Outlying measurements have slight
- (2.938475665748384e-2%)
- effect on estimated standard deviation.
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- lower bound |
- estimate |
- upper bound |
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-
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- OLS regression |
- xxx |
- xxx |
- xxx |
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- R² goodness-of-fit |
- xxx |
- xxx |
- xxx |
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- Mean execution time |
- 8.139915127469478e-2 |
- 8.203977915769449e-2 |
- 8.294207157255142e-2 |
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- Standard deviation |
- 8.712272680628149e-4 |
- 1.303323554210239e-3 |
- 1.75288845055683e-3 |
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- Outlying measurements have slight
- (9.000000000000001e-2%)
- effect on estimated standard deviation.
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- |
- lower bound |
- estimate |
- upper bound |
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-
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- OLS regression |
- xxx |
- xxx |
- xxx |
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- R² goodness-of-fit |
- xxx |
- xxx |
- xxx |
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- Mean execution time |
- 3.6837263323658102e-3 |
- 3.690185989917211e-3 |
- 3.7016024806948455e-3 |
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- Standard deviation |
- 1.87435230068336e-5 |
- 2.7406270029965376e-5 |
- 3.868689618608243e-5 |
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- Outlying measurements have slight
- (2.1266540642722116e-2%)
- effect on estimated standard deviation.
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- |
- lower bound |
- estimate |
- upper bound |
-
-
-
- OLS regression |
- xxx |
- xxx |
- xxx |
-
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- R² goodness-of-fit |
- xxx |
- xxx |
- xxx |
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- Mean execution time |
- 3.539779838485038e-2 |
- 3.5467452958929724e-2 |
- 3.5585300045038584e-2 |
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- Standard deviation |
- 1.1602363892840695e-4 |
- 1.603301186146598e-4 |
- 2.1296560804213925e-4 |
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- Outlying measurements have slight
- (5.8593749999999986e-2%)
- effect on estimated standard deviation.
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- |
- lower bound |
- estimate |
- upper bound |
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-
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- OLS regression |
- xxx |
- xxx |
- xxx |
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- R² goodness-of-fit |
- xxx |
- xxx |
- xxx |
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- Mean execution time |
- 0.3519412043621479 |
- 0.35207846750875343 |
- 0.3521320771337429 |
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- Standard deviation |
- 0.0 |
- 1.2712910344311448e-4 |
- 1.4489214967143587e-4 |
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- Outlying measurements have moderate
- (0.1875%)
- effect on estimated standard deviation.
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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.
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- - 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.
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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.
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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.)
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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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