splitmix alternatives and similar packages
Based on the "System" category.
Alternatively, view splitmix alternatives based on common mentions on social networks and blogs.
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ghc-hotswap
DISCONTINUED. Example code for how we swap compiled code within a running Haskell process. -
plugins
Dynamic linking and runtime evaluation of Haskell, and C, including dependency chasing and package resolution. -
ascii-progress
A simple Haskell progress bar for the console. Heavily borrows from TJ Holowaychuk's Node.JS project -
language-puppet
A library to work with Puppet manifests, test them and eventually replace everything ruby.
SaaSHub - Software Alternatives and Reviews
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README
splitmix
Pure Haskell implementation of SplitMix pseudo-random number generator.
dieharder
Dieharder is a random number generator (rng) testing suite. It is intended to test generators, not files of possibly random numbers as the latter is a fallacious view of what it means to be random. Is the number 7 random? If it is generated by a random process, it might be. If it is made up to serve the purpose of some argument (like this one) it is not. Perfect random number generators produce "unlikely" sequences of random numbers – at exactly the right average rate. Testing a rng is therefore quite subtle.
time $(cabal-plan list-bin splitmix-dieharder) splitmix
The test-suite takes around half-an-hour to complete. From 30 runs, 2.49% were weak (3247 passed, 83 weak, 0 failed).
In comparison, built-in Marsenne Twister test takes around 15min.
time dieharder -a
benchmarks
benchmarking list 64/random
time 1.317 ms (1.303 ms .. 1.335 ms)
0.998 R² (0.998 R² .. 0.999 R²)
mean 1.380 ms (1.365 ms .. 1.411 ms)
std dev 70.83 μs (37.26 μs .. 131.8 μs)
variance introduced by outliers: 39% (moderately inflated)
benchmarking list 64/tf-random
time 141.1 μs (140.4 μs .. 142.1 μs)
0.999 R² (0.998 R² .. 1.000 R²)
mean 145.9 μs (144.6 μs .. 150.4 μs)
std dev 7.131 μs (3.461 μs .. 14.75 μs)
variance introduced by outliers: 49% (moderately inflated)
benchmarking list 64/splitmix
time 17.86 μs (17.72 μs .. 18.01 μs)
0.999 R² (0.998 R² .. 1.000 R²)
mean 17.95 μs (17.75 μs .. 18.47 μs)
std dev 1.000 μs (444.1 ns .. 1.887 μs)
variance introduced by outliers: 64% (severely inflated)
benchmarking tree 64/random
time 800.3 μs (793.3 μs .. 806.5 μs)
0.999 R² (0.998 R² .. 0.999 R²)
mean 803.2 μs (798.1 μs .. 811.2 μs)
std dev 22.09 μs (14.69 μs .. 35.47 μs)
variance introduced by outliers: 18% (moderately inflated)
benchmarking tree 64/tf-random
time 179.0 μs (176.6 μs .. 180.7 μs)
0.999 R² (0.998 R² .. 0.999 R²)
mean 172.7 μs (171.3 μs .. 174.6 μs)
std dev 5.590 μs (4.919 μs .. 6.382 μs)
variance introduced by outliers: 29% (moderately inflated)
benchmarking tree 64/splitmix
time 51.54 μs (51.01 μs .. 52.15 μs)
0.999 R² (0.998 R² .. 0.999 R²)
mean 52.50 μs (51.93 μs .. 53.55 μs)
std dev 2.603 μs (1.659 μs .. 4.338 μs)
variance introduced by outliers: 55% (severely inflated)
Note: the performance can be potentially further improved when GHC gets SIMD Support.