Welcome to the dieharder distribution website. Version 3.29.4beta is the current snapshot. Some of the documentation below may not quite be caught up to it, but it should be close. 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 rando
AWS Open Source Blog Better Random Number Generation for OpenSSL, libc, and Linux Mainline 䏿ç In 2015, AWS introduced s2n, a new open source implementation of the TLS/SSL protocols that protect the privacy and integrity of data moving over a network. s2n was designed to be secure, simple, small, and fast. The project is thriving, and we use it extensively. In February, our CISO Stephen Schmidt sh
NOTICES This is a beta release of the 2.0 Version of the Beacon Service. The first release and data is available at: NIST Randomness Beacon (Version 1.0) May 17, 2024, between 6:30-10:00PM ET new pulses from the Beacon Service may be unavailable. Any pulses generated during any interruption will be available as soon as all services are restored. NIST Randomness Beacon (Version 2.0 Beta) -- work in
This page describes some new pseudorandom number generators (PRNGs) I have been working on recently with David Blackman, and a shootout comparing them with other generators. Details about the generators can be found in our paper. Information about my previous xorshift-based generators can be found here, but they have been entirely superseded by the new ones, which are faster and better. As part of
This article was ported from my old Wordpress blog here, If you see any issues with the rendering or layout, please send me an email. papadHard to believeSanjeev Arora and his coauthors consider it âa basic tool [that should be] taught to all algorithms students together with divide-and-conquer, dynamic programming, and random sampling.â Christos Papadimitriou calls it âso hard to believe that it
* For the PCG family, arbitrary k-dimensional equidistribution (and the huge periods it implies) requires PCG's extended generation scheme. â ChaCha entry based on an optimized C++ implementation of ChaCha, kindly provided by Orson Peters. Random Number Generation Is Important Algorithmic random number generators are everywhere, used for all kinds of tasks, from simulation to computational creativ
So it is four times faster! No bad. As usual, my code is freely available. What can this be good for? Well⦠if you have been forcing your arrays and hash tables to have power-of-two capacities to avoid expensive divisions, you may be able to use the fast range map to support arbitrary capacities without too much of a performance penalty. You can also generate random numbers in a range faster, whic
There are a few things about /dev/urandom and /dev/random that are repeated again and again. Still they are false. /dev/urandom is insecure. Always use /dev/random for cryptographic purposes. Fact: /dev/urandom is the preferred source of cryptographic randomness on UNIX-like systems. /dev/urandom is a pseudo random number generator, a PRNG, while /dev/random is a âtrueâ random number generator. Fa
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A Few Thoughts on Cryptographic Engineering Some random thoughts about crypto. Notes from a course I teach. Pictures of my dachshunds. I'm a cryptographer and professor at Johns Hopkins University. I've designed and analyzed cryptographic systems used in wireless networks, payment systems and digital content protection platforms. In my research I look at the various ways cryptography can be used t
haveged - A simple entropy daemon Introduction The haveged project is an attempt to provide an easy-to-use, unpredictable random number generator based upon an adaptation of the HAVEGE algorithm. Haveged was created to remedy low-entropy conditions in the Linux random device that can occur under some workloads, especially on headless servers. Current development of haveged is directed towards impr
I've written an article on Secure Random Numbers for the August 2002 issue of Windows Developer Magazine. During the research phase, I found that while a lot of resources were available online (mostly in the form of link farms), it was difficult to decide what to read and what not. I ended up reading most of what I found. To make the job easier for people trying to learn about secure random number
Example of shuffling five letters using Durstenfeld's in-place version of the FisherâYates shuffle The FisherâYates shuffle is an algorithm for shuffling a finite sequence. The algorithm takes a list of all the elements of the sequence, and continually determines the next element in the shuffled sequence by randomly drawing an element from the list until no elements remain.[1] The algorithm produc
Reservoir sampling is a family of randomized algorithms for choosing a simple random sample, without replacement, of k items from a population of unknown size n in a single pass over the items. The size of the population n is not known to the algorithm and is typically too large for all n items to fit into main memory. The population is revealed to the algorithm over time, and the algorithm cannot
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