How to Install and Uninstall ghc-splitmix Package on openSuSE Tumbleweed

Last updated: November 23,2024

1. Install "ghc-splitmix" package

This guide let you learn how to install ghc-splitmix on openSuSE Tumbleweed


    
        
            $
        

        
            
                sudo zypper refresh
            
                    
    
    Copied


    
        
            $
        

        
            
                sudo zypper install
            
                        
                ghc-splitmix
            
                    
    
    Copied

2. Uninstall "ghc-splitmix" package

Please follow the guidelines below to uninstall ghc-splitmix on openSuSE Tumbleweed:


    
        
            $
        

        
            
                sudo zypper remove
            
                        
                ghc-splitmix
            
                    
    
    Copied

3. Information about the ghc-splitmix package on openSuSE Tumbleweed

Information for package ghc-splitmix:
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Repository : openSUSE-Tumbleweed-Oss
Name : ghc-splitmix
Version : 0.1.0.5-1.4
Arch : x86_64
Vendor : openSUSE
Installed Size : 104.1 KiB
Installed : No
Status : not installed
Source package : ghc-splitmix-0.1.0.5-1.4.src
Upstream URL : https://hackage.haskell.org/package/splitmix
Summary : Fast Splittable PRNG
Description :
Pure Haskell implementation of SplitMix described in
Guy L. Steele, Jr., Doug Lea, and Christine H. Flood. 2014. Fast splittable
pseudorandom number generators. In Proceedings of the 2014 ACM International
Conference on Object Oriented Programming Systems Languages & Applications
(OOPSLA '14). ACM, New York, NY, USA, 453-472. DOI:

The paper describes a new algorithm /SplitMix/ for /splittable/ pseudorandom
number generator that is quite fast: 9 64 bit arithmetic/logical operations per
64 bits generated.
/SplitMix/ is tested with two standard statistical test suites (DieHarder and
TestU01, this implementation only using the former) and it appears to be
adequate for "everyday" use, such as Monte Carlo algorithms and randomized data
structures where speed is important.
In particular, it __should not be used for cryptographic or security
applications__, because generated sequences of pseudorandom values are too
predictable (the mixing functions are easily inverted, and two successive
outputs suffice to reconstruct the internal state).