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Mersenne twister -- 目前为止最好的随机数算法

Mersenne twister -- From Wikipedia, the free encyclopedia

The Mersenne twister is a pseudorandom number generator developed in 1997 by Makoto Matsumoto (松本 , Makoto Matsumoto?) and Takuji Nishimura (西村 拓士, Takuji Nishimura?)[1] that is based on a matrix linear recurrence over a finite binary field F2. It provides for fast generation of very high-quality pseudorandom numbers, having been designed specifically to rectify many of the flaws found in older algorithms.

Its name derives from the fact that period length is chosen to be a Mersenne prime. There are at least two common variants of the algorithm, differing only in the size of the Mersenne primes used. The newer and more commonly used one is the Mersenne Twister MT19937, with 32-bit word length. There is also a variant with 64-bit word length, MT19937-64, which generates a different sequence.


Unlike Blum Blum Shub, the algorithm in its native form is not suitable for cryptography. Observing a sufficient number of iterates (624 in the case of MT19937) allows one to predict all future iterates.

Another issue is that it can take a long time to turn a non-random initial state into output that passes randomness tests, due to its size. A small lagged Fibonacci generator or linear congruential generator gets started much quicker and usually is used to seed the Mersenne Twister. If only a few numbers are required and standards aren't high it is simpler to use the seed generator. But the Mersenne Twister will still work.

For many other applications, however, the Mersenne twister is quickly becoming the pseudorandom number generator of choice[citation needed]. Since the library is portable, freely available and quickly generates good quality pseudorandom numbers it is rarely a bad choice.

It is designed with Monte Carlo simulations and other statistical simulations in mind. Researchers primarily want good quality numbers but also benefit from its speed and portability.

The commonly used variant of Mersenne Twister, MT19937 has the following desirable properties:

  1. It was designed to have a period of 219937 − 1 (the creators of the algorithm proved this property). In practice, there is little reason to use larger ones, as most applications do not require 219937 unique combinations (219937 is approximately 4.3 × 106001).
  2. It has a very high order of dimensional equidistribution (see linear congruential generator). This implies that there is negligible serial correlation between successive values in the output sequence.
  3. It passes numerous tests for statistical randomness, including the Diehard tests. It passes most, but not all, of the even more stringent TestU01 Crush randomness tests.

The Mersenne Twister algorithm has received some criticism in the computer science field, notably by George Marsaglia. These critics claim that while it is good at generating random numbers, it is not very elegant and is overly complex to implement. Marsaglia has provided several examples of random number generators that are less complex yet which he claims provide significantly larger periods. For example, a simple complementary multiply-with-carry generator can have a period 1033000 times as long, be significantly faster, and maintain better or equal randomness.[2][3]

Algorithmic detail

The Mersenne Twister algorithm is a twisted generalised feedback shift register[4] (twisted GFSR, or TGFSR) of rational normal form (TGFSR(R)), with state bit reflection and tempering. It is characterized by the following quantities:

  • w: word size (in number of bits)
  • n: degree of recurrence
  • m: middle word, or the number of parallel sequences, 1 ≤ mn
  • r: separation point of one word, or the number of bits of the lower bitmask, 0 ≤ rw - 1
  • a: coefficients of the rational normal form twist matrix
  • b, c: TGFSR(R) tempering bitmasks
  • s, t: TGFSR(R) tempering bit shifts
  • u, l: additional Mersenne Twister tempering bit shifts

with the restriction that 2nw − r − 1 is a Mersenne prime. This choice simplifies the primitivity test and k-distribution test that are needed in the parameter search.

For a word x with w bit width, it is expressed as the recurrence relation

with | as the bitwise or and as the bitwise exclusive or (XOR), xu, xl being x with upper and lower bitmasks applied. The twist transformation A is defined in rational normal form

with In − 1 as the (n − 1) × (n − 1) identity matrix (and in contrast to normal matrix multiplication, bitwise XOR replaces addition). The rational normal form has the benefit that it can be efficiently expressed as


In order to achieve the 2nw − r − 1 theoretical upper limit of the period in a TGFSR, φB(t) must be a primitive polynomial, φB(t) being the characteristic polynomial of

The twist transformation improves the classical GFSR with the following key properties:

  • Period reaches the theoretical upper limit 2nw − r − 1 (except if initialized with 0)
  • Equidistribution in n dimensions (e.g. linear congruential generators can at best manage reasonable distribution in 5 dimensions)

As like TGFSR(R), the Mersenne Twister is cascaded with a tempering transform to compensate for the reduced dimensionality of equidistribution (because of the choice of A being in the rational normal form), which is equivalent to the transformation A = RA = T−1RT, T invertible. The tempering is defined in the case of Mersenne Twister as

y := x  (x >> u)

y := :y  ((y << s) & b)

y := :y  ((y << t) & c)

z := y  (y >> l)

with <<, >> as the bitwise left and right shifts, and & as the bitwise and. The first and last transforms are added in order to improve lower bit equidistribution. From the property of TGFSR, is required to reach the upper bound of equidistribution for the upper bits.

The coefficients for MT19937 are:

  • (w, n, m, r) = (32, 624, 397, 31)
  • a = 9908B0DF16
  • u = 11
  • (s, b) = (7, 9D2C568016)
  • (t, c) = (15, EFC6000016)
  • l = 18


The following generates uniformly 32-bit integers in the range [0, 232 − 1] with the MT19937 algorithm:

 // Create a length 624 array to store the state of the generator

 int[0..623] MT

 int index = 0


 // Initialize the generator from a seed

 function initializeGenerator(int seed) {

     MT[0] := seed

     for i from 1 to 623 { // loop over each other element

         MT[i] := last 32 bits of(1812433253 * (MT[i-1] xor (right shift by 30 bits(MT[i-1]))) + i) // 0x6c078965




 // Extract a tempered pseudorandom number based on the index-th value,

 // calling generateNumbers() every 624 numbers

 function extractNumber() {

     if index == 0 {




     int y := MT[index]

     y := y xor (right shift by 11 bits(y))

     y := y xor (left shift by 7 bits(y) and (2636928640)) // 0x9d2c5680

     y := y xor (left shift by 15 bits(y) and (4022730752)) // 0xefc60000

     y := y xor (right shift by 18 bits(y))


     index := (index + 1) mod 624

     return y



 // Generate an array of 624 untempered numbers

 function generateNumbers() {

     for i from 0 to 623 {

         int y := 32nd bit of(MT[i]) + last 31 bits of(MT[(i+1) mod 624])

         MT[i] := MT[(i + 397) mod 624] xor (right shift by 1 bit(y))

         if (y mod 2) == 1 { // y is odd

             MT[i] := MT[i] xor (2567483615) // 0x9908b0df





SFMT, the SIMD-oriented Fast Mersenne Twister, is a variant of Mersenne Twister, introduced in 2006[5], designed to be fast when it runs on 128-bit SIMD.

  • It is roughly twice as fast as Mersenne Twister.[6]
  • It has a better equidistribution property of v-bit accuracy than MT but worse than WELL ("Well Equidistributed Long-period Linear").
  • It has quicker recovery from zero-excess initial state than MT, but slower than WELL.
  • It supports various periods from 2607-1 to 2216091-1.

Intel SSE2 and PowerPC AltiVec are supported by SFMT. It is also used for games with the Cell BE in the Playstation 3.[7]


  1. ^ M. Matsumoto & T. Nishimura, "Mersenne twister: a 623-dimensionally equidistributed uniform pseudorandom number generator", ACM Trans. Model. Comput. Simul. 8, 3 (1998).
  2. ^ Marsaglia on Mersenne Twister 2003
  3. ^ Marsaglia on Mersenne Twister 2005
  4. ^ M. Matsumoto & Y. Kurita, "Twisted GFSR generators", ACM Trans. Model. Comput. Simul. 2, 179 (1992); 4, 254 (1994).
  5. ^ SIMD-oriented Fast Mersenne Twister (SFMT)
  6. ^ SFMT:Comparison of speed
  7. ^ PLAYSTATION 3 License

External links



posted on 2009-01-19 18:58 Chipset 阅读(12899) 评论(16)  编辑 收藏 引用 所属分类: 转载


# re: Mersenne twister -- 目前为止最好的随机数算法 2009-07-28 10:07 nice

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# re: Mersenne twister -- 目前为止最好的随机数算法 2009-12-10 17:01 徐万夫

您好,想请教一下,这个算法的623维和n=624是什么关系呢?因为产生的随机数是依次循环624个状态产生的,而19937 = 623*32+1,2^19937-1必定会重复32位的数623次,那么怎样区分这623次(维)呢?  回复  更多评论   

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# re: Mersenne twister -- 目前为止最好的随机数算法 2011-10-18 15:16 scofined

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# re: Mersenne twister -- 目前为止最好的随机数算法[未登录] 2011-10-20 08:48 chipset

遵循什么分布需要用到概率里面的分布知识。随机数产生器产生的随机数未必是均匀分布,除非你用数学方法让它产生均匀分布。这里只是最原始的原理,MT的周期也未必一定是2^19937 - 1,可以修改一下,让周期更长或更短。

常用的分布有:均匀分布、泊松分布、二项分布、正态分布、三角分布等等有很多种。你可以参考一下boost里的random,boost里面有三种随机数产生器,MT仅仅是其中一种。  回复  更多评论   

# re: Mersenne twister -- 目前为止最好的随机数算法 2012-06-24 15:07 牟滔

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