无限精度整数：除以2

Question

在C中，如果我想将int除以2，x%2应该运行得像(x%10)% 2 一样快，因为好的编译器只会查看最后一位。但是如何用无限精度的算术语言呢？

特别是在Haskell中，它会更快（或者它们会是相同的速度）：even x或even (quot x 10)？

Answer 1

好吧，我会咬：

import Control.DeepSeq 
import Criterion.Main 
import Data.Bits 
import System.Random 

lotsOfBigNumbers :: [Integer] 
lotsOfBigNumbers = take 10000 $ randomRs (2^128, 2^132) (mkStdGen 42) 

evenRem, evenBits :: Integer -> Bool 
evenRem x = even (x `rem` 10) 
evenBits x = (x .&. 1) == 0 
remRem x = ((x `rem` 10) `rem` 2) == 0 

main = do 
    deepseq lotsOfBigNumbers (return()) 
    defaultMain 
     [ bench "even"  $ nf (map even ) lotsOfBigNumbers 
     , bench "evenRem" $ nf (map evenRem) lotsOfBigNumbers 
     , bench "evenBits" $ nf (map evenBits) lotsOfBigNumbers 
     , bench "remRem" $ nf (map remRem ) lotsOfBigNumbers 
     ] 

而且结果：

sorghum:~/programming% ghc -O2 test && ./test 
[1 of 1] Compiling Main    (test.hs, test.o) 
Linking test ... 
warming up 
estimating clock resolution... 
mean is 1.920340 us (320001 iterations) 
found 51108 outliers among 319999 samples (16.0%) 
    46741 (14.6%) low severe 
    4367 (1.4%) high severe 
estimating cost of a clock call... 
mean is 83.20445 ns (16 iterations) 
found 4 outliers among 16 samples (25.0%) 
    2 (12.5%) low mild 
    2 (12.5%) high severe 

benchmarking even 
mean: 1.508542 ms, lb 1.503661 ms, ub 1.514950 ms, ci 0.950 
std dev: 28.53574 us, lb 23.35796 us, ub 35.19769 us, ci 0.950 
found 18 outliers among 100 samples (18.0%) 
    17 (17.0%) high severe 
variance introduced by outliers: 11.371% 
variance is moderately inflated by outliers 

benchmarking evenRem 
mean: 1.937735 ms, lb 1.930118 ms, ub 1.949699 ms, ci 0.950 
std dev: 48.17240 us, lb 34.95334 us, ub 71.22055 us, ci 0.950 
found 14 outliers among 100 samples (14.0%) 
    3 (3.0%) high mild 
    11 (11.0%) high severe 
variance introduced by outliers: 18.989% 
variance is moderately inflated by outliers 

benchmarking evenBits 
mean: 996.3537 us, lb 992.2839 us, ub 1.003864 ms, ci 0.950 
std dev: 27.37875 us, lb 17.51923 us, ub 43.98499 us, ci 0.950 
found 15 outliers among 100 samples (15.0%) 
    2 (2.0%) high mild 
    13 (13.0%) high severe 
variance introduced by outliers: 21.905% 
variance is moderately inflated by outliers 

benchmarking remRem 
mean: 1.925495 ms, lb 1.918590 ms, ub 1.935869 ms, ci 0.950 
std dev: 43.00092 us, lb 31.67173 us, ub 57.83841 us, ci 0.950 
found 13 outliers among 100 samples (13.0%) 
    13 (13.0%) high severe 
variance introduced by outliers: 15.198% 
variance is moderately inflated by outliers 

结论：一个额外的费用rem多一点，和.&.费用少一点。