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