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

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 Information about my previous xorshift-based generators can be found here, but they have been entirely superseded by the new ones, which are faster and better.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 “a basic tool [that should be] taught to all algorithms students together with divide-and-conquer, dynamic programming, and random sampling.” Christos Papadimitriou calls it “so 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. † 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

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 “true” random number generator. Fa

[PR]上記の広告は3ヶ月以上新規記事投稿のないブログに表示されています。新しい記事を書く事で広告が消えます。 2014年9月4日 にあった、ＣＥＤＥＣの講演についての記事です。 ゲーム世界を動かすサイコロの正体 ～ 往年のナムコタイトルから学ぶ乱数の進化と応用 バンダイナムコのプログラマさんより乱数の講演です。 ・ゲームで使われている主要な乱数のしくみについて ・乱数のアルゴリズムが改良されていく歴史 ・乱数の選択の注意点 などについて、取り上げられました。 （乱数分布などは　せっかくなので　初め、自分で乱数プログラム組んで　用意しようかと思ったのですが 力尽きてしまい・・・ 撮影した写真を使わせていただきました） -------------------------------------------------------------------- ●はじめに ・真の乱数 →　実際のサ

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

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

Ｃ言語標準ライブラリの乱数rand( )は質に問題があり、禁止している学会もある。 他にも乱数には様々なアルゴリズムがあるが、多くのものが問題を持っている。 最も多くの人に使われている乱数であろう Visual Basic の Rnd の質は最低である。 そもそも乱数とは 乱数とは、本来サイコロを振って出る目から得られるような数を意味する。 このような乱数は予測不能なものである。 しかし、計算機を使って乱数を発生させた場合、 次に出る数は完全に決まっているので、予測不能とはいえない。 そこで、計算機で作り出される乱数を疑似乱数(PRNG)と呼び区別することがある。 ここでは、特にことわらない限り乱数とは疑似乱数のことを指すとする。 計算機でソフト的に乱数を発生させることの最大のメリットは、 再現性があることである。 初期状態が同じであれば、発生する乱数も全く同じものが得られる。 このことは