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# XorShift Jump 101, Part 1: Matrix Multiplication

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Jump forward/backward procedures for XorShift RNG explained step by step
This is a step by step tutorial explaining the math foundations and C++ implementation of the jump forward/backward procedures for the xorshift random number generators family. Part 1 is dedicated to algorithm based on matrix multiplication. Transition matrix is calculated automatically from the RNG formula definition with symbolic transformations implemented in Haskell.

## Introduction

This article is a sequel to System.Random and Infinite Monkey Theorem, where we explored the internal structure of the standard random number generator (RNG) from the .NET Framework. Using the theory of linear RNGs, we learned how to navigate the sequence of random numbers jumping $N$ steps forward or backward with logarithmic complexity. This time, we are going to develop similar functionality for another family of linear RNGs, known as xorshift.

We will start from building transition matrices for this RNG. Unlike lagged Fibonacci generators, these matrices have more complex structure so we would prefer to calculate them automatically. This calculation is performed by a program written in a powerful but not very popular language, Haskell. Giving a test drive to Haskell was one of my goals for this research and it appeared to be worth the efforts. After that, we will consider another method of jumping based on polynomial arithmetics [2] which has been mentioned in the previous article but not tried yet. Finally, we will compare these two approaches.

Putting all the material in a single article would made it a bit too long to read at once, so I've decided to split it into two parts. Part 1 is dedicated to matrix-based approach, while part 2 will consider the polynomial-based algorithm and performance testing. For the same reason, I will focus on math background and general ideas rather than software implementation. The exception is made for code generator in Haskell: assuming this language is not widely used, I stepped through each line of the code explaining it in more details.

Why do we need one more RNG algorithm? Unfortunately, System.Random is not perfect. First of all, statistical testing exposed some problems immanent to the whole family of lagged Fibonacci generators [3, §3.2.2], [4, ch. 7]. Besides that, a critical bug has been found in the .NET implementation of this algorithm: lag values specified in the code don't guarantee the longest period possible. In fact, I don't know any theoretical estimate of the period length for these lags. This issue has been discussed many times by the open source .NET community (#5974, #12746, #23298) but the code remains as is for the sake of backward compatibility. (See also an excellent research made by Søren Fuglede Jørgensen.)

Taking these facts into account, I wouldn't recommend using System.Random if you need not only random numbers but also some kind of proven estimate of their properties.

Now it would be natural to ask a question: why have we explored System.Random being aware of its bad sides? The answer is simple: while being bad for the real-world use, this RNG is good to explain the theory. Indeed, lagged Fibonacci algorithm uses only simple integer arithmetics and its transition matrices are clear enough to be written directly without complex calculations. Also, the state vector of this RNG is sufficiently large to keep a text message, the trick which brought my previous article to life.

## Background

It is assumed that the reader is familiar with linear RNG theory basics explained in System.Random and Infinite Monkey Theorem. Unlike that article, the demo code for this one is written in C++ 11 so the reader should be fluent in this dialect at the intermediate level.

Polynomial-based approach requires some additional knowledge in the basics of algebra, such as the notion of polynomial, its properties and operations involving polynomials. Software implementation is based on NTL, a library of number theory methods developed by Victor Shoup. This library, in turn, needs GNU GMP and GF2X. Versions used for this article are NTL 11.0.0, GMP 6.1.2, and GF2X 1.2, respectively.

While NTL documentation mentions that it is possible to build this library with Visual C++, the recommended environment should be Unix-style, such as G++ (with accompanying toolchain) under Linux or Cygwin (I used this one).

Some C++ code for this article has been generated automatically by a program written in Haskell. Source code archive includes ready-to-use output of this generator so the reader doesn't necessarily have to run it. Otherwise, some working Haskell system, e.g., Haskell Platform, would be needed. Readers not familiar with Haskell would also need some beginner-level guide. I used Yet Another Haskell Tutorial by Hal Daumé III.

Google Test 1.8.1 is used for unit testing infrastructure.

## First Steps

Let's begin from implementing basic RNG functionality for xorshift algorithm.

First of all, we should choose some particular algorithm from this family to be used in our studies. Let's implement xor128 defined by George Marsaglia in [5, p. 5].

All code related to basic RNG functions would be placed to file xs.cc with forward declaration of defined entities in header file xs.h. The important design trade-off has been made in this code which is popular in research programming: we won't follow the encapsulation principles of the OOP for the sake of maximum flexibility and expose all internal state of the RNG and other objects to public scope. Also, I would prefer using bare'' data structures processed by non-member functions. Again, this is a research programming trade-off: when you do a research, you often need to define some alternative actions on data, and it is convenient to do right in place where these actions are used instead of adding more and more class members to some common class definition or bothering with inheritance.

Marsaglia defines the internal state of xor128 as four separate 32-bit variables: x, y, z, and w, respectively. Since we are going to apply our matrix-based approach, this state should be treated as single 128-bit vector. It would be convenient to pack them into 4-element vector of 32-bit unsigned integers to perform matrix operations uniformly but sometimes we will use x, y, z, and w as aliases for corresponding vector items when they are treated differently.

C++
using state_t = std::array<uint32_t, 4>;

This state vector can be initialized by arbitrary value except all zeroes but originating paper defines some canonical'' initial seed which we will use as well.

C++
void init(state_t &s)
{
s[0] = 123456789;
s[1] = 362436069;
s[2] = 521288629;
s[3] = 88675123;
}

The next step would be implementing functions to produce random numbers from this state. This process can be divided into two sub-problems: we should mutate RNG state to its next value and then produce the next item of output using this new state. Common practice is putting these two into one piece of code (Marsaglia's paper follows it, too) but in our research, we need to be able to separate these actions. Indeed, we have to be able to either do the next step as usual or compute the next state using some jump algorithm and then resume producing random numbers from that state like it has been obtained during previous access to the generator. So, we will define two separate functions:

C++
void step_forward(state_t &s)
{
uint32_t &x = s[0], &y = s[1], &z = s[2], &w = s[3];

uint32_t t = x ^ (x << 11);

x = y; y = z; z = w;
w = w ^ (w >> 19) ^ (t ^ (t >> 8));
}

uint32_t gen_u32(state_t &s)
{
step_forward(s);

return s[3];
}

## Matrix-Based Approach

After defining step_forward(), it's good time now to discuss why xorshift RNGs belong to linear family. Section Checking for linearity involves some amount of math so readers more interested in programming can skip it till the final conclusion. Next section, Calculating transition matrix, provides step by step explanation on how to generate needed transition matrix programmatically.

### Checking for Linearity

Our goal will be to show that single step of xorshift method can be expressed as $S^{(i+1)} = TS^{(i)}$, where $T$ is a constant matrix known as transition matrix and $S^{(i)}$ is a state vector at step $i$. For xorshift both $S$ and $T$ are defined over $\mathbb{F}_2$: $S \in \mathbb{F}_2^{N}$, and $T \in \mathbb{F}_2^{N\times N}$, where $N$ is a size of state vector ($N=128$ for the rest of this article). In programmers' parlance, it means that items of these vectors or matrices are single bits and allowed arithmetical operations are multiplication (bitwise and'') and addition modulo $2$ (bitwise xor'').

To simplify our formulas, let's omit the step index and denote $S'=S^{(i+1)}$ and $S=S^{(i)}$. Our transition formula then would be $S' = TS$. Also, let's use lowercase letters with indices for vector and matrix items, such as $s_i$ or $t_{i,j}$, $0\le i < N$, $0\le j < N$.

Given that $S'=TS$, every component of $S'$ is

Let's begin from parts of updated state vector named x, y, z. They cover state vector bits from $s_0$ till $s_{95}$ and are obtained by copying other 32-bit chunks as is:

$s'_i = s_{i+32}, \qquad 0 \le i < 96.$

Therefore, $75\%$ of our transition matrix rows would be like this:

$t_{i,j} = \begin{cases} 1, & j = i + 32,\\ 0, &\mbox{otherwise}\\ \end{cases}, \qquad 0 \le i < 96, 0 \le j < 128,$

satisfying the requirements.

On the other hand, chunk w is calculated in a more complicated manner: we can see a composition of bitwise xor'' operations with bitwise shifts. Writing exact formulas for each component would be time-consuming so we would apply induction:

1. The innermost elements of the formulas are state vector components, so they match the structure of ($\ref{eq:component}$).
2. Given subexpressions matching ($\ref{eq:component}$), we can apply addition modulo 2 and get the sum satisfying the same condition (this follows from properties of addition).
3. Given a vector of subexpressions matching ($\ref{eq:component}$), we can see that shift operation either replaces subexpression for one component by subexpression for another, keeping property ($\ref{eq:component}$) intact, or replaces it with zero which is also conformant to that condition.

Putting it all together, we can conclude that transformations defined in step_forward() are indeed suitable for matrix-based jump algorithms described in my previous article.

### Calculating Transition Matrix

As mentioned before, it is possible to calculate transition matrix $T$ from code implementing xorshift RNG. To do that, we need to write formulas for each operation in the code componentwise, i.e., for every bit of state vector $S$. This is tedious and error-prone. Even worse, since xorshift is a family of RNGs, we will have to calculate matrix $T$ for every kind of xorshift from the very beginning. The solution is simple: let's write a program to do that for us automatically.

This program should take some formulas describing given kind of xorshift and transform those formulas using rules of arithmetics into the form ($\ref{eq:component}$). Doing that for every of $N$ bits of the state vector $S$, we will get $N\times N$ items of the matrix $T$.

Symbolic manipulations with formulas require appropriate language to implement them. The main data structure to process would be a tree-like structure whose nodes represent each constant, variable or operation and their interaction in formulas. Functional language Haskell seems be an appropriate tool due to its first-class support for recursive data structures, algebraic data types and pattern matching.

Also, Haskell provides a powerful feature to help with making experiments and rapid prototyping, known as REPL (Read-eval-print-loop). Using REPL, we can implement our matrix generator interactively, specifying type and function definitions one by one and use them immediately from the interactive Haskell shell to see how they work.

Source code archive provided with the article contains file xsTgen.hs. I'm going to explain how it works step by step. To do that, we will load this file into the Haskell interpreter, named ghci if you are using the recommended Haskell Platform. After that, we will enter some Haskell expressions to the REPL and examine their output. Note that development process was very similar: I wrote function definitions one by one and tested them immediately. (This process is much easier if you use Haskell-aware editor, e.g. Emacs.) Such feedback is very useful when you do exploratory programming.

To start the interpreter, open command-line prompt window, change current directory to be one containing our source file, xsTgen.hs and run ghci. If everything went fine, you should see this kind of start-up message:

GHCi, version 8.6.3: http://www.haskell.org/ghc/  :? for help
Prelude>

Load source code using command :l:

Prelude> :l xsT
[1 of 1] Compiling Main             ( xsTgen.hs, interpreted )
*Main>

Windows users may also start WinGHCi as GUI application and load source file via menu.

Let's begin our tour from data type representing formulas of xorshift transformations:

data Expr =
Zero
| Var String Integer
| Xor [Expr]
deriving (Eq, Show)

Here, we have a tagged union which may store either constant zero or variable referenced by name with index or a set of arbitrary expressions xor'ed together. The clause deriving tells Haskell to create some helper code for our data type automatically.

We can see now the information about defined data type:

*Main> :i Expr
data Expr = Zero | Var String Integer | Xor [Expr]
-- Defined at xsTgen.hs:1:1
instance [safe] Show Expr -- Defined at xsTgen.hs:5:17
instance [safe] Eq Expr -- Defined at xsTgen.hs:5:13

and type some expressions to see how it works:

*Main> Zero
Zero
*Main> Var "x" 1
Var "x" 1
*Main> Xor [Var "x" 1, Var "y" 2]
Xor [Var "x" 1,Var "y" 2]
*Main> Xor [Var "x" 1, Xor[Zero, Var "y" 2]]
Xor [Var "x" 1,Xor [Zero,Var "y" 2]]

These expressions define internal representation for such formulas as: $0$, $x_1$, $x_1\oplus y_2$, $x_1\oplus (0\oplus y_2)$, respectively. Note that our Expr is not enough to represent the complete $\mathbb{F}_2$ arithmetics: we don't have constant $1$ or multiplication to write things like $y_1\oplus 1$ or $x_1\cdot y_2$. This is a bit restrictive but we don't need such ability to solve our current problem.

Now it's time to teach our code to process vectors of expressions so we won't have to write formulas for each bit of RNG state. If we look at the xsTgen.hs, data type Expr is followed by this set of definitions:

bus name cntr = map (Var name) [0 .. cntr - 1]

zeroes = Zero : zeroes

xor x y = zipWith (\x y -> Xor [x, y]) x y

crop x y = zipWith (\ x y -> x) x y

shl x n = crop (take n zeroes ++ x) x

shr x n = crop (drop n x ++ zeroes) x

These equations define some simple functions. Let's see how they work. Function bus is used to create a vector of indexed variables given their name and size:

*Main> bus "x" 3
[Var "x" 0,Var "x" 1,Var "x" 2]
*Main> bus "y" 4
[Var "y" 0,Var "y" 1,Var "y" 2,Var "y" 3]

Helper function zeroes produces an infinite sequence of zero constants. Therefore, we can't evaluate it as is but we can consume some specified number of zeroes when needed:

*Main> take 5 zeroes
[Zero,Zero,Zero,Zero,Zero]
*Main> take 7 zeroes
[Zero,Zero,Zero,Zero,Zero,Zero,Zero]

Function Xor takes two expression vectors and produces a new one xoring them component-wise:

*Main> xor (bus "x" 3) (bus "y" 3)
[Xor [Var "x" 0,Var "y" 0],Xor [Var "x" 1,Var "y" 1],Xor [Var "x" 2,Var "y" 2]]

One more helper function crop takes two expression vectors and returns the components of the first vector taken up to the length of the second one:

*Main> crop (bus "x" 4) (bus "y" 3)
[Var "x" 0,Var "x" 1,Var "x" 2]

Using this helper, we are now able to define two shift functions operating on expression vectors moving their components:

*Main> shl (bus "x" 4) 2
[Zero,Zero,Var "x" 0,Var "x" 1]
*Main> shr (bus "x" 4) 2
[Var "x" 2,Var "x" 3,Zero,Zero]

Note that function names seem counter-intuitive if we look at their results as expression vectors per se but they would be more clear if we interpret vector components as bits in the word so shifting them left indeed means shifting towards the most-significant bit.

Using these primitives, we can define xorshift-like transformations with formulas quite close to the original C or C++ code:

*Main> let x = bus "x" 3
*Main> let y = bus "y" 3
*Main> xor x (shl y 2) ++ y
[Xor [Var "x" 0,Zero],Xor [Var "x" 1,Zero],Xor [Var "x" 2,Var "y" 0],
Var "y" 0,Var "y" 1,Var "y" 2]

We can also use one more feature of Haskell, the ability to treat functions like binary operations enclosing their names in backquotes:

*Main> x xor (y shl 2) ++ y
[Xor [Var "x" 0,Zero],Xor [Var "x" 1,Zero],Xor [Var "x" 2,Var "y" 0],
Var "y" 0,Var "y" 1,Var "y" 2]

In fact, we created a kind of mini-DSL for our problem.

Now it's time to learn how to transform our expressions. The ultimate goal would be a linear form ($\ref{eq:component}$) which is directly convertible to transition matrix.

Looking at the definition of type Expr, we can see that its values are trees where leaves can be either zeroes or variable components and all inner nodes are Xor operations applied to subtrees. In math, we express this with formulas using nested subexpressions: $x_1 \oplus (x_2 \oplus (y_3 \oplus \ldots))$ or $x_1 \oplus ((x_2 \oplus y_3) \oplus \ldots))$.

Taking into account properties of addition modulo 2, we can convert these expressions to the linear form which would be the same for both of them: $x_1 \oplus x_2 \oplus y_3 \oplus \ldots$ Function flatten works exactly this way:

flatten::Expr -> [Expr]
flatten (Xor (x:xs)) = flatten x ++ (concatMap flatten xs)
flatten (Xor [])   = []
flatten x          = [x]

It takes an expression tree and returns a sequence of primitives (constants or variables) which are implicitly xor'ed together. To do that, we traverse the expression tree recursively and for each Xor operation node we flatten each subtree and concatenate them together. Other kinds of nodes are collected into this list as is.

Let's apply this function to expression trees described above:

*Main> flatten (Xor [Var "x" 1, Xor [Var "x" 2, Xor [Var "y" 3, Zero]]])
[Var "x" 1,Var "x" 2,Var "y" 3,Zero]
*Main> flatten (Xor [Var "x" 1, Xor [Xor[Var "x" 2, Var "y" 3], Zero]])
[Var "x" 1,Var "x" 2,Var "y" 3,Zero]

(Since our expression trees are not suited to describe incomplete formulas, I replaced the ellipsis with zero constant to make them well-formed.) Also note that we can get duplicates after flattening:

*Main> flatten (Xor [Var "x" 1, Xor [Xor[Var "x" 2, Var "x" 1], Zero]])
[Var "x" 1,Var "x" 2,Var "x" 1,Zero]

We've got a significant progress on the way towards our final goal! We are able now to define xorshift transformations in a concise form and convert formulas to linear sequence of involved variables. Our next step would be to convert those lists of variables to vectors of coefficients which, in turn, will become rows of transition matrix.

Let's define one more function:

matrix e vars = zipWith (\e v -> (v, makeRow e)) e vars where
makeRow e = map (\v -> length (filter (== v) e) mod 2) vars

Parameter e is a list of expression lists [[Expr]]. Each list item corresponds to single bit of state vector and this item is a list of Var or Zero items produced by flatten. Second parameter, vars, is a list of Var where $i$-th item denotes variable corresponding to the $i$-th item of the state vector. We have already introduced x and y to be our building blocks and we can evaluate them in REPL to recall their values. Now it's time to define a formula we are going to process:

*Main> x
[Var "x" 0,Var "x" 1,Var "x" 2]
*Main> y
[Var "y" 0,Var "y" 1,Var "y" 2]
*Main> let e = x xor (y shl 2) ++ y
*Main> let v = x ++ y
*Main> e
[Xor [Var "x" 0,Zero],Xor [Var "x" 1,Zero],Xor [Var "x" 2,Var "y" 0],Var "y" 0,
Var "y" 1,Var "y" 2]
*Main> v
[Var "x" 0,Var "x" 1,Var "x" 2,Var "y" 0,Var "y" 1,Var "y" 2]

Here, we have two vector variables, x and y. These three-bit vectors are combined to state vector $S=\langle x_0, x_1, x_2, y_0, y_1, y_2\rangle$. Expression e is a xorshift-like transformation over these vectors. To get the first argument of matrix, we should apply flatten to expression e componentwise. Variable v is just a collection of all components from x and y, listed in one sequence as needed by the second argument of matrix.

*Main> matrix (map flatten e) v
[(Var "x" 0,[1,0,0,0,0,0]),(Var "x" 1,[0,1,0,0,0,0]),(Var "x" 2,[0,0,1,1,0,0]),
(Var "y" 0,[0,0,0,1,0,0]),(Var "y" 1,[0,0,0,0,1,0]),(Var "y" 2,[0,0,0,0,0,1])]

Great! The output looks exactly as specified by ($\ref{eq:component}$): for each bit of the state vector we have a vector of coefficients.

To see how matrix works, let's choose some particular state vector item to examine. Let it be the $x_2$ since its coefficient vector has 2 non-zero items while others have only one. We should prepare it first:

*Main> let e2 =  flatten (e!!2)
*Main> e2
[Var "x" 2,Var "y" 0]

Since we are going to consider the single state bit, calculating matrix would effectively be the same as calculating makeRow with appropriate arguments, which in turn may be replaced by its definition:

*Main> map (\v' -> length (filter (== v') e2) mod 2) v
[0,0,1,1,0,0]

(Note that I had to replace function argument name v to v' to avoid conflict with the top-level name used to keep list of involved variables.)

Now let's see what will happen if we apply the innermost expression:

*Main> v
[Var "x" 0,Var "x" 1,Var "x" 2,Var "y" 0,Var "y" 1,Var "y" 2]
*Main> map (\v' -> filter (== v') e2) v
[[],[],[Var "x" 2],[Var "y" 0],[],[]]

As you can see, each variable mentioned in v maps to a list containing itself and the rest of variables is mapped to empty list.

Adding one more level, we wrap the innermost filter with length:

*Main> map (\v' -> length (filter (== v') e2)) v
[0,0,1,1,0,0]

Looks like what we needed! But we have also taken a modulo $2$ as the final stroke in matrix definition. We have to do that to cover the case when some variable is referred more than once in the same expression. Variables with even number of occurrences should disappear due to mutual cancellation of these terms and taking modulo $2$ does exactly that: all even counts become zeroes and all odd counts become ones.

A word of caution: Looking at our code, we can see that each expression list out of $N$ lists (where $N$ is a state vector length) is matched against the list of variables which is also of length $N$. This gives us the resulting complexity of $O(N^2)$. We could probably improve it using sorted expression lists but since this code is not intended to be run often, let's trade its efficiency for simplicity.

The problem of getting transition matrix for given xorshift transformation has been solved. Some technical issues still need their resolution, however. First of all, we need to pack our bit matrix to integers for future use in C++ code. This goal is achieved with the following function:

packMatrix m vars = map ($v, b) -> (v, tail (packRow b vars "" 0))) m where packRow xb@(b:bs) xv@((Var n i):vs) vname acc | n == vname = packRow bs vs vname (acc + b*2^i) | otherwise = acc : packRow xb xv n 0 packRow [] _ _ acc = [acc] packRow _ [] _ acc = [acc] Let's test it: *Main> packMatrix (matrix (map flatten e) v) v [(Var "x" 0,[1,0]),(Var "x" 1,[2,0]),(Var "x" 2,[4,1]),(Var "y" 0,[0,1]), (Var "y" 1,[0,2]),(Var "y" 2,[0,4])] Function packMatrix takes each row of the transition matrix constructed with function matrix and groups bits corresponding to the same family of variables. That is, if we have two families of variables in our expression, \(x_i$ and $y_i$, we will have two integer words per matrix row. Within the same word, bit weight corresponds to variable index, e.g., bit row item for $x_i$ takes the $i$-th bit within word representing all $x$ components. For example, in matrix row for Var "x" 2 we see [4, 1]. First word is for $x$, and its the only non-zero bit has position $2$ (counting from zero). Indeed, the expression for $x_2$ includes $x_2$ itself. Doing the same with the second word, we can see that another term would be $y_0$.

Next function will take a packed transition matrix and generate C++ code for array initializer:

matrixCode pm = header ++ (concat \$ intersperse ",\n"
(map ($v, r) -> " {" ++ hexRow r ++ "}") pm)) ++ "\n};\n" where header = printf "#include <cstdint>\n\nuint32_t T[%d][%d] = \ \{\n" (length pm) (length  snd  head pm) hexRow r = concat  intersperse ", " (map (printf "0x%08XU") r) Its result looks like this: *Main> matrixCode  packMatrix (matrix (map flatten e) v) v "#include <cstdint>\n\nuint32_t T[6][2] = {\n {0x00000001U, 0x00000000U},\n {0 x00000002U, 0x00000000U},\n {0x00000004U, 0x00000001U},\n {0x00000000U, 0x0000 0001U},\n {0x00000000U, 0x00000002U},\n {0x00000000U, 0x00000004U}\n};\n" We are almost done! Let's define main function: main = do putStr (matrixCode  packMatrix (matrix (map flatten xs128) vars) vars) where x = bus "x" 32 y = bus "y" 32 z = bus "z" 32 w = bus "w" 32 vars = (x++y++z++w) t = x xor (x shl 11) xs128 = y ++ z ++ w ++ (w xor (w shr 19) xor t xor (t shr 8)) Here, we have a xor128 definition written very similar to its original C code. We apply our transformations as described above and put the resulting string to the standard output. That's all! Special rule should also be added to Makefile to automate using this generator: xsT.cc: xsTgen.hs (HS) xsTgen.hs > xsT.cc where makefile variable HS points to runhaskell program from Haskell Platform or its equivalent from other Haskell implementation of your choice. It means that xsT.cc will only be generated when missing or when generator itself has been modified. Source code archive for this article contains pre-generated xsT.cc so you will be able to build C++ code without installing Haskell. ### Forward Step Jumping \(k$ steps forward from RNG state $S$ if you know transition matrix $T$ is as easy as calculating $T^kS$. (See my previous article for details.) So, we need implementing these arithmetical primitives first. Let's define transition matrix representation in xs.h as:

C++
using tr_t = uint32_t[STATE_SIZE_EXP][STATE_SIZE];

and our next chunk of code in xs.cc would be:

C++
inline uint32_t get_bit(const tr_t A, size_t row, size_t col)
...
inline void set_bit(tr_t A, size_t row, size_t col, uint32_t val)
...
void mat_mul(tr_t C, const tr_t A, const tr_t B)
...
void mat_pow(tr_t B, const tr_t A, uint64_t n)

This code is more or less straightforward as long as you are familiar with matrix arithmetics, exponentiation by squaring, and bit manipulations in C++. Therefore, I would just briefly describe some implementation trade-offs made to keep it more clear than efficient:

• Our matrix $T$ uses packed representation, where each integer word corresponds to many matrix items, one bit per item. Therefore, using bitwise logical operations in C++, we could process many items in parallel, but in my code for this article, I traded efficiency for clarity. That is, I simply defined access functions for one-bit matrix items, get_bit and set_bit, and implemented trivial $O(N^3)$ matrix multiplication on top of these accessors.
• One more tradeoff was using internal temporary storage in matrix multiplication. Without these temporary variables, caller would be responsible for avoiding aliases, i.e., function calls like this: mat_mul(B, A, B), there multiplication result is written over one of the arguments. Putting the caller on duty for allocating non-aliased buffers could probably be more efficient but less clear.
• Function mat_pow calculating matrix power takes its exponent as 64-bit integer. This is not enough to cover all possible step lengths for xor128 but handling multiple-word numbers would make code less clear.

### Backward Step

Stepping backward with transition matrices uses one more special matrix which is inverse to transition matrix $T$. That is, we need to calculate $T^{-1}$ such that $TT^{-1}=I$, where $I$ is identity matrix.

While it is possible to obtain $T^{-1}$ from $T$ solving matrix equations or with some other matrix inversion algorithm (which could be quite difficult to do in $\mathbb{F}_2$), there is simpler way based on xorshift properties. We know that period of this RNG is $2^N-1$, where $N$ is the number of state bits. It means that starting from some state $S$ and doing $2^N-1$ steps, we should get that state $S$ back. In our matrix notation, this can be written as $T^PS = S$, where $P = 2^N-1$. Since $IS = S$ by definition of identity matrix, we can conclude that $T^P = I$. We can also write the latter equation as $TT^{P-1} = I$ and finally conclude that $T^{-1} = T^{P-1}$.

The same result can be obtained without this math. Indeed, if doing $P$ steps brings us back to initial step $S$, what should happen if we do one step less? We should end up with a state $S^*$ such that the next step will make it $S$, i.e. $TS^*=S$. So, this state $S^*$ is a direct precedent to $S$, as needed, and we can jump to it immediately using $T^{P-1}$ as transition matrix.

After figuring out how $T^{-1}$ could be calculated, the implementation is obvious. We could use our mat_pow function to get an appropriate power of $T$, but as you can remember, we limited the exponent value to be 64-bit for simplicity. So, I've implemented separate function, calculate_bwd_transition_matrix, which effectively calculates matrix exponentiation but with hardcoded exponent value $2^N-2$. This value has simple binary representation: $N-1$ ones followed by single zero. Therefore, code in mat_pow checking for next bit being one can be replaced by test of bit position.

This initialization should be somehow performed before we do any backward step. In production code, we should use precalculated constant value like we do for matrix $T$, but in our proof-of-concept code, I'm calculating it before the first usage. This makes first backward step significantly slower than forward one.

### Generic Matrix-based Transition Functions

For convenience, there are two functions wrapping all matrix manipulations into simple interface:

C++
enum class Direction {FWD, BWD};

void prepare_transition(tr_t T_k, uint64_t k, Direction dir);

void do_transition(state_t &s, const tr_t T);

First function calculates transition matrix for $k$ steps using appropriate single-step matrix depending from direction specified as parameter. It looks attractive to make parameter $k$ signed integer and pass negative values to jump backward, but this idea has its own drawback: even with 128-bit integers, we will need full unsigned precision to represent all possible jump sizes, so it would be better to pass direction as separate parameter.

Second function, do_transition, simply takes matrix prepared by prepare_transition and applies it to given RNG state.

Making these two transition phases separate is needed if you are going to perform multiple jumps with the same number of steps. Since matrix exponentiation is more expensive than applying transition matrix to RNG state, we can precalculate transition matrix once and re-use it many times later.

## Pit Stop

First part of the article comes to its end. We have refreshed our background in matrix-based jump algorithms and applied it to one more family of RNGs. Using functional language Haskell to generate transition matrix has been very unusual but interesting adventure. With Haskell, convenient DSL-like notation describing xorshift formula can be developed in a few lines of code. This code may look quite esoteric but using read-eval-print loop, the popular, if not standard, feature of Haskell implementations, you can develop and try it part by part and step by step.

In the second part, we will dive deeper in the mysteries of algebra and learn how polynomials can be used to solve this problem in a completely different way.

## References

[1] Hiroshi Haramoto, Makoto Matsumoto, and Pierre L’Ecuyer. A fast jump ahead algorithm for linear recurrences in a polynomial space. In Proceedings of the 5th International Conference on Sequences and Their Applications, SETA ’08, pages 290–298, Berlin, Heidelberg, 2008. Springer-Verlag. (Available online.)

[2] Hiroshi Haramoto, Makoto Matsumoto, Takuji Nishimura, François Panneton, and Pierre L’Ecuyer. Efficient jump ahead for $\mathbb{F}_2$-linear random number generators. INFORMS Journal on Computing, 20(3):385–390, 2008. (Available online.)

[3] Donald E. Knuth. The Art of Computer Programming, Volume 2 (3rd Ed.): Seminumerical Algorithms. Addison-Wesley Longman Publishing Co., Inc., Boston, MA, USA, 1997.

[4] P. L’Ecuyer and R. Simard. TestU01: A C library for empirical testing of random number generators. ACM Transactions on Mathematical Software, 33(4): Article 22, August 2007.

[5] George Marsaglia. Xorshift RNGs. Journal of Statistical Software, Articles, 8(14):1–6, 2003. (Available online.)

## History

• 12th April, 2020: Initial version

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