criterion performance measurements

overview

want to understand this report?

sum $ map (+ 1) $ map (* 2) $ enumFromTo 1 9001/vegito

lower bound estimate upper bound
OLS regression xxx xxx xxx
R² goodness-of-fit xxx xxx xxx
Mean execution time 5.04698339805408e-6 5.058126555367965e-6 5.0814215532222215e-6
Standard deviation 2.7559530528929736e-8 4.6512743123217627e-8 8.761050021819734e-8

Outlying measurements have no (5.5864158565838064e-3%) effect on estimated standard deviation.

sum $ map (+ 1) $ map (* 2) $ enumFromTo 1 9001/gotenks

lower bound estimate upper bound
OLS regression xxx xxx xxx
R² goodness-of-fit xxx xxx xxx
Mean execution time 5.062140922903756e-6 5.075952813887726e-6 5.091200413636181e-6
Standard deviation 3.9371246343271035e-8 4.880622795629583e-8 6.44440402052526e-8

Outlying measurements have slight (5.5542819141926165e-2%) effect on estimated standard deviation.

sum $ map (+ 1) $ map (* 2) $ enumFromTo 1 9001/conduit-combinators unqualified

lower bound estimate upper bound
OLS regression xxx xxx xxx
R² goodness-of-fit xxx xxx xxx
Mean execution time 1.1181095312286869e-3 1.1356401409082068e-3 1.1661312170029943e-3
Standard deviation 5.3390468744207256e-5 8.533695308299818e-5 1.281909475729944e-4

Outlying measurements have severe (0.5881477694601457%) effect on estimated standard deviation.

sum $ map (+ 1) $ map (* 2) $ enumFromTo 1 9001/conduit-combinators qualified

lower bound estimate upper bound
OLS regression xxx xxx xxx
R² goodness-of-fit xxx xxx xxx
Mean execution time 5.06693941595305e-6 5.1026746014408814e-6 5.1950427538039535e-6
Standard deviation 5.648093624150736e-8 1.6225630266956213e-7 3.0510118826434783e-7

Outlying measurements have moderate (0.3955108305055059%) effect on estimated standard deviation.

sum $ map (+ 1) $ map (* 2) $ enumFromTo 1 9001/conduit

lower bound estimate upper bound
OLS regression xxx xxx xxx
R² goodness-of-fit xxx xxx xxx
Mean execution time 5.062207883103922e-6 5.081944961114066e-6 5.153059118355966e-6
Standard deviation 3.5085619081380075e-8 1.2020458513794528e-7 2.4623421455957536e-7

Outlying measurements have moderate (0.2685189553749743%) effect on estimated standard deviation.

sum $ map (+ 1) $ map (* 2) $ enumFromTo 1 9001/vector boxed

lower bound estimate upper bound
OLS regression xxx xxx xxx
R² goodness-of-fit xxx xxx xxx
Mean execution time 5.057778935483031e-6 5.0697247966163474e-6 5.092574020921399e-6
Standard deviation 3.186354317873175e-8 5.314405713977064e-8 8.628427042088357e-8

Outlying measurements have slight (6.877276137113449e-2%) effect on estimated standard deviation.

sum $ map (+ 1) $ map (* 2) $ enumFromTo 1 9001/vector unboxed

lower bound estimate upper bound
OLS regression xxx xxx xxx
R² goodness-of-fit xxx xxx xxx
Mean execution time 5.0769111591411775e-6 5.104424164005706e-6 5.198288106302256e-6
Standard deviation 5.1897927901574893e-8 1.4082341994381395e-7 2.98641675387848e-7

Outlying measurements have moderate (0.3291521152030871%) effect on estimated standard deviation.

sum $ map (+ 1) $ map (* 2) $ enumFromTo 1 9001/vector unboxed foldM

lower bound estimate upper bound
OLS regression xxx xxx xxx
R² goodness-of-fit xxx xxx xxx
Mean execution time 5.05937659811085e-6 5.073262967569812e-6 5.092720320952292e-6
Standard deviation 4.3930310039045714e-8 5.576834742633988e-8 7.51249398088904e-8

Outlying measurements have slight (7.41578693737416e-2%) effect on estimated standard deviation.

sum $ map (+ 1) $ map (* 2) $ enumFromTo 1 9001/direct implementation

lower bound estimate upper bound
OLS regression xxx xxx xxx
R² goodness-of-fit xxx xxx xxx
Mean execution time 5.980777495909067e-6 6.001788421220449e-6 6.0363379192345105e-6
Standard deviation 5.97640859602847e-8 8.224398460349541e-8 1.2313198130959798e-7

Outlying measurements have moderate (0.108624445414539%) 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.

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.

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.