# Using Wang Tiles to Simulate Turing Machines

Wang tiles were invented by Hao Wang in 1961 for mathematical reasons, but they find great use in games for making tile based art which gives results that don’t look tiled – both with 2d tiled textures, as well as 3d tiled models.

Apparently Wang tiles are also able to execute Turing machines, and so are thus Turing complete – meaning they can execute any program.

That is a pretty amazing and perplexing statement, so this post explores that a bit.

## Wang Tiles Overview

Wang tiles are rectangular tiles where each edge will only fit with other specific edges, but that for any specific edge, there is more than one possible tile that can fit with that edge. By fit with that edge, I mean they are seamless when put together, without any visual artifacts to hint at there actually being a seam between the tiles.

This is useful for graphics because this lets you have seamless tiled graphics, but the specific configuration of how the tiles are placed can be completely randomized, so long as their edges are all compatible. The result is tiled graphics that doesn’t look at all tiled, due to visible patterns being much less noticeable than with traditional tiled graphics.

Here is an example I made. The graphics are programmer art but hopefully you get the idea. This was made with 16 tiles, where there were two different edge types per edge.

## Turing Machine Overview

Turing machines were invented in 1936 by Alan Turing as a generic computing machine that was proven to be able to execute any algorithm.

The turing machine is made up of a few main components: the memory tape, the read/write head, and the state machine.

The memory tape is infinitely long, so has infinite storage, and is initialized to all zeroes to start out.

The read/write head starts at a position on the tape, and can read or write values, and also move left or right on the tape.

The state machine knows what state it is in and has rules about what to do in each state when it reads a value from the tape.

For instance, in state A, if a 0 is read from the tape, the rule may be to write a 1 to the current position on the tape, move the read/write head to the right, and go to state B. State B may have completely different logic, and could either transition back to state A, state in state B, or move to another state entirely.

Using simple state transition logic like that, any computer algorithm can be performed.

In a Turing machine there can also be a “Halt State” which means the program is finished executing and the answer it was trying to calculate has been calculated.

Looking at some programs, you can easily see that they will halt eventually, or that they will be an infinite loop and never halt. Some programs in-between are complex and can’t very easily be determined if they will ever halt or not. Turing proved that there is no general solution to whether a Turing machine (aka any computer program) will halt or not, and this is called the Halting Problem. In general, the only way to know if a program will halt or not is to wait and see. So, effectively the answers to whether a program in general will halt or not are “yes” and “not yet” – although for many specific programs you can in fact see that they will halt eventually if you were to run them.

## Wang Tile Computation

It turns out that Wang tiles can simulate a Turing machine, and so are “Turing complete” meaning that they too can perform any computer algorithm.

To make this happen, we’ll make a column of tiles that represent the state of the Turing machine at a specific point in time, starting with time 0 at the left most column. We’ll place tiles in the column to the right making sure all edge rules are respected, and then do the column to the right of that one etc until the program halts (or forever if it doesn’t halt). If we set up our set of tiles correctly, the act of satisfying the edge rules as we place our tiles is enough to execute the Turing machine.

Let’s walk through a simple example where we have the following state machine logic rules:

1. When in state A, if a 0 is read, we will write a 1, move the read/write head down and move to state B.
2. When in state A, if a 1 is read, we will halt (enter the halt state).
3. When in state B, if a 0 is read, we will write a 1, move the read/write head up and move to state A.
4. When in state B, if a 1 is read, we will halt (enter the halt state).

### Tape Memory Storage

The first thing we need is persistent storage of memory for the tape. For this, we’ll need the following two tiles:

To see this working, we can set up a section of tape with some values (make a column of wang tiles), and we can see that the only valid wang tiles to place next to the starting column are tiles which propogate the 0 and the 1 values forward in time without modifying them.

In the diagram below, we initialize the tape to 0101 in the left most column (time 0). By only placing down tiles with compatible edges you can see that our memory values persist forever. Our memory storage is implemented, huzzah!

We’ll start our example with all memory initialized to 0, but the above shows that we can have persistent memory.

The read/write head of the Turing machine is represented as part of the edge information. In this way, besides an edge storing the 0 or 1, if that is where the read/write head is, it also stores the state of the state machine.

Our example uses two states (besides the halt state): A and B. If a 1 is read in while being in either state A or B, the program halts.

To handle that, we need the tiles below:

Now that we have the rules for entering the halt state done (rule #2 and rule #4), we have to figure out how to implement the rules that control switching from one state to another (rule #1 and rule #3).

Rule #1 says that if we are in state A and read a 0, we should write a 1, move the read/write head down and move to state B.

We’ll need this tile to cause reading a 0 in state A to write a 1 as output, and to tell the tile below to move to state B.

The tile below that one could either be a 0 or a 1, and without knowing which, we want it to keep it’s value but accept the read/write head and be in state B. To do that we need two tiles, one for if there is a 0 on the tape at that position, and another for if there is a 1 on the tape.

Rule #3 says that if we are in state B and read a 0, we should write a 1, move the read/write head up and move to state A.

To do that, we’ll need a similar construction as for rule #1 but we are moving up instead of down. These 3 tiles will give us what we need:

## Starting Column Tiles

We are going to treat the boundaries of our simulation area as if they have edges of “x”.

This means that to make our starting column (the Turing machine at time 0), we are going to need 2 special tiles. One tile will be for storing a 0 on the tape, which is what the tape is initialized to, and the other tile will be for storing the position of the read/write head in state A, which is our starting state.

Here are those two tiles:

## Final Tileset

Here’s the full set of 12 tiles that we are going to use:

## Full Simulation

Here is the initial setup at time 0 for our Turing machine. Note that this is one possible starting state, but this is the starting state we are choosing. We are not leaving it up to chance where the read/write head starts, or if it is even present at all. If we only followed edge rules we may get 4 read/write heads or 0, or anything in between.

From here, to build the second column, we start from the top and work towards the bottom, choosing the tile that fits the constraints of the edge it touches. In this first step, the head reads a 0, writes a 1, moves down, and moves to state B.

Heres is the second step, where the read reads a 0, writes a 1, moves up, and moves to state A.

Here is the final step, where the head reads a 1 and enters the halt state, signifying that the program has terminated.

The program halted, and gave an output value of “0110” or 6. This output isn’t really meaningful but other programs can give output that is meaningful. For instance you could have a Turing machine add two numbers, and the output would be the sum of those two numbers.

## An Important Detail

There is an important detail that the above doesn’t address, and that many explanations of Wang tile Turing machines don’t seem to talk about.

When placing the second tile for time 2, the only constraint from the edges is that the tile must have an x on top and a 1 on the left. This actually makes it ambiguous which tile should be chosen between the two tiles below.

How do we choose the right one then?

The answer is that you make a guess and just choose one. If the wrong one was chosen in this case, when we moved to the next tile, we’d be looking for a tile which had an x on top and a B0 on the left. There is no such tile so you’d be unable to place a tile. When this happened, you’d take a step back to the last tile, and try one of the other possibilities.

So, unfortunately there is some literal trial and error involved when simulating Turing machines with Wang tiles, but it is fairly manageable at least. It definitely makes it a bit more complex to calculate in a pixel shader if you were so inclined (or other massively parallel processing units), but it shouldn’t be that much more costly.

Some of the links below talk about Wang tiles and Turing machines, but don’t seem to strictly be Turing machines anymore. For instance, you might notice that some examples allow data to travel “back in time” where after the program halts, the answer is in the tape at time 0 of the Turing machine, even though that data wasn’t actually there at time 0. This shows that Wang tiles can do computation in their own right, beyond simulating Turing machines, but I’m not really sure what that technique itself would be called.

Also if you are wondering if this is useful to do computation with Wang tiles, I’m not really sure of any practical usage cases myself. However, apparently scientists have found that DNA can act much like Wang tiles act, where they will fit together only if edges are compatible. Because of this, there is ongoing research into DNA based computation that is based on the work of Wang tile computation. pretty interesting stuff!

Here is a shadertoy implementation of wang tiles computing prime numbers in a webgl pixel shader:

Here are some great videos on Turing machines and the halting problem:
Turing Machines Explained – Computerphile
Turing & The Halting Problem – Computerphile

Computing With Tiles
Wikipedia: Wang Tile
Wang Tiles and Turing Machines
Wang Tiles – 1

Computing With Tiles
Computability of Tilings

# A Sixth Way To Calculate Sine Without Trig

I have another item to add to the pile of ways to calculate sine without trig. Here are the previous ways before we start:

Four Ways to Calculate Sine Without Trig

A Fifth Way to Calculate Sine Without Trig

This method is called Bhaskara I’s sine approximation formula and it’s just a numerical way of approximating sine.

The below is some glsl code from @paniq that has been adapted to take 0 to 1 as input, which corresponds to 0 to 2*pi radians, or 0 to 360 degrees, and returns the normalized vector of that angle. The x component of the vector is the cosine of the angle and the y component of the vector is the sine of the angle. Useful for packing 2d normals into a color channel (;

// https://en.wikipedia.org/wiki/Bhaskara_I%27s_sine_approximation_formula
// x is 0..1 corresponding to 0..360 degrees
vec2 CosSin(float x) {
vec2 si = fract(vec2(0.5,1.0) - x*2.0)*2.0 - 1.0;
vec2 so = sign(0.5-fract(vec2(0.25,0.5) - x));
return (20.0 / (si*si + 4.0) - 4.0) * so;
}


Here’s a shadertoy to compare/contrast this technique versus reality (or, reality as per the video card). Spoiler alert – the shadertoy is ridiculous, they are basically the same.

More from paniq:

i also wrote an approximate atan to go with it Shadertoy: Pseudo-Polar Mapping see the ALTMETHOD branch. also changed the sin/cos computation to ensure the sin/cos vector is perfectly normalized.

# Matrix Form of Bezier Curves

Bezier curves are most often talked about either in terms of the De Casteljau algorithm, or in terms of a mathematical function (Bernstein Polynomials).

Every now and then though, you see people talking about Bezier curves being calculated via matrices. If you ever wondered what that was all about, this post should hopefully explain and demystify that a bit.

If you don’t know how to come up with the equation of a Bezier curve for any number of control points, you should give this a read first:
Easy Binomial Expansion & Bezier Curve Formulas

And if you are curious about the De Casteljau algorithm, you can learn about that here:
The De Casteljau Algorithm for Evaluating Bezier Curves

Ok, all read up on that stuff? Let’s get talking about Bezier curves in matrix form! There are shadertoy links at the end with working wegl glsl demos that include source code.

## Making the Matrix Form of Bezier Curves

Coming up with the matrix for a Bezier curve is surprisingly easy. Keep in mind the matrix we are making is for glsl which is a column major matrix order, so you might have to adjust things if you are using a row major matrix order setup (mostly, just transpose the matrix).

The first step is to get the formula for a Bezier curve. We’ll work through the example using a quadratic Bezier curve with 3 control points A,B,C, so we start with the formula below:

$f(t) = A*(1-t)^2 + B*2t(1-t) + C*t^2$

The next step is to break the equation into one equation per term. Each term has a control point, so we are basically splitting the formula up so that we have one formula per control point.

$A*(1-t)^2 \\ B*2t(1-t) \\ C*t^2$

Next, we remove the control points and expand each term to get:

$1-2t+t^2 \\ 2t-2t^2 \\ t^2$

Now, explicitly values of all powers of t that are present:
$1*t^0-2*t^1+1*t^2 \\ 0*t^0+2*t^1-2*t^2 \\ 0*t^0+0*t^1+1*t^2$

Now the final step. Take the constants that multiply your powers of t and make a matrix out of them. You are done!

$\begin{bmatrix} 1 & -2 & 1 \\ 0 & 2 & -2 \\ 0 & 0 & 1 \\ \end{bmatrix}$

## Using the Matrix Form

Using the matrix form of Bezier curves is also pretty simple.

First, we need to make a vector of the power series of our t value:

$powerSeries = \begin{bmatrix} t^0 & t^1 & t^2 \\ \end{bmatrix}$

Which can also be written as:

$powerSeries = \begin{bmatrix} 1 & t & t^2 \\ \end{bmatrix}$

You also need a vector of your control points:

$controlPoints = \begin{bmatrix} A & B & C \\ \end{bmatrix}$

You next perform this operation to get a result vector:

$result = powerSeries * curveMatrix * controlPoints$

Then, you add up all components of result to get the value of the curve at time t.

$value = result[0] + result[1] + result[2]$

All done!

Note that this is a one dimensional Bezier curve. You need to do this operation once per axis to get your final multi dimensional Bezier curve point.

If you are confused by that last line, check out this post: One Dimensional Bezier Curves

## Multiplying the Control Points In

You might notice that if you are evaluating several points on the same curve that you are going to be multiplying the curveMatrix matrix by the controlPoints vector over and over. You can multiply the control points into the Bezier curve matrix to make the specific matrix for those control points if you want to. You multiply the columns of the matrix by the control points, and adjust the result calculation like the below.

// Multiply the control points into the curve matrix
curveMatrix[0] *= A;
curveMatrix[1] *= B;
curveMatrix[2] *= C;

// Use the curve matrix that has the control points baked in, to do less math to get the result vector.
// You would calculate the curve matrix once and re-use it multiple times of course!
vec3 result = powerSeries * curveMatrix;
float value = result.x + result.y + result.z;


## Closing

You might wonder when you’d use the matrix form. One time to use the matrix form would be when you had fast matrix math support (like on the GPU). Another time to use the matrix form though is if you ever want to cut up a Bezier curve into multiple smaller sub curves. The matrix form can help make that easier, and you can read more about that here if you want: A Matrix Formulation of the Cubic Bezier Curve

Here are some shadertoys that show this all working in webgl/glsl pixel shaders, along with source code:

# Actually Making Signed Distance Field Textures With JFA

This post is an addendum to the last post where I say that you can make distance field textures with JFA but don’t fully explain how to make SIGNED distance field textures, which is what you really want.

If you want to go straight to a working demo with webgl pixel shader source code, here is the shadertoy: Shadertoy: JFA SDF Texture

If you naively use a distance transform to make a distance field texture, you’ll get an UNSIGNED distance field texture, where you only have the distance to the surface of the object from the outside, but won’t have the distance to the surface of the object from the inside.

This is important because signed distance field textures have both, and use bilinear interpolation of distance on each side of the shape surface to make a nice smooth line. Below is what happens when you try to use an unsigned distance field texture (aka the distance transform of the image, using JFA / Voronoi information), using the zero distance line as the surface of the object:

It looks ok (if not fairly pixelated), but you can really see it break down when you zoom in:

So you might say to yourself, maybe i need to keep the surface line at distance 0.5 instead of 0.0 so that there is distance information to interpolate? If you do that, the first thing you might notice is that the objects get fatter:

But it does look better when you zoom in, which is a plus:

The real issue is that you really just need the distance from each pixel to the surface of the object from both the inside and the outside. In our case, our Voronoi diagram we make with JFA only gives the distance from the outside. So what is the solution? At first I was thinking maybe you can get the gradient of this data at the point of each pixel and “push the zero line in” a little bit to give at least one pixel layer worth of inside data. However, a brilliant friend of mine came up with the actual solution: You invert your source data so empty space becomes seed, and seed becomes empty space, and you run JFA again to get the distance from the inside!

That actually works very well. It’s also very easy to combine them. You make a pixel shader that reads the data from the outside Voronoi diagram and the inside Voronoi diagram, calculate the output distance (0.5 + outsideDistance * 0.5 – insideDistance * 0.5), and output that 0 to 1 distance value in one or more of the color channels.

Here’s a glsl excerpt below, note that we divide the distance by 8 and clamp between 0 and 1 so that the data is suitable for a normalized color image (normalized as in the color channels can store values between 0 and 1):

// calculate distances from seed coordinates
float outsideDist = clamp(length(outsideSeedCoord-fragCoord) / 8.0, 0.0, 1.0);
float insideDist  = clamp(length(insideSeedCoord-fFragCoord)  / 8.0, 0.0, 1.0);

// calculate output distance
float signedDistance = 0.5 + outsideDist * 0.5 - insideDist * 0.5;

// set the color based on that distance
fragColor = vec4(signedDistance);


It actually looks a lot like the first image where we use the zero distance line of the unsigned distance field texture, so we still aren’t quite there:

When you zoom in, it looks a little better, but something still seems a bit off:

The final step to making this look good is to realize that the power of signed distance textures is in their ability to interpolate distance information well. When we have a full resolution texture, there is no interpolation going on. We actually need to decrease the size of our distance field texture to make it look better. If only all problems were solved by making textures smaller!

Here is the resulting image when making the distance field texture 1/4 as large on each axis (1/16th as big total):

And zooming in you can see that it scales very well. The zoom is a 20x magnification, on top of the magnification we already get from it being a larger texture:

And just to show the intermediary textures, here is the outside distance Voronoi diagram:

And the inside distance Voronoi diagram (The seed is in bright green, the dim green is the empty space that has distance information):

And here is the final distance field texture used to render the final result I showed above.

Zoomed in to show just how low resolution it is! This is the thing that looks like a + or a sword just left of middle.

Again, here is the shadertoy that does this technique, generating a signed distance field texture on the fly for randomly placed objects, and then using that signed distance field to render a larger image that you can further zoom in to: