A GPU Approach to Path Finding

Last time I demonstrated how to run Conway’s Game of Life entirely on a graphics card. This concept can be generalized to any cellular automaton, including automata with more than two states. In this article I’m going to exploit this to solve the shortest path problem for two-dimensional grids entirely on a GPU. It will be just as fast as traditional searches on a CPU.

The JavaScript side of things is essentially the same as before — two textures with fragment shader in between that steps the automaton forward — so I won’t be repeating myself. The only parts that have changed are the cell state encoding (to express all automaton states) and the fragment shader (to code the new rules).

Included is a pure JavaScript implementation of the cellular automaton (State.js) that I used for debugging and experimentation, but it doesn’t actually get used in the demo. A fragment shader (12state.frag) encodes the full automaton rules for the GPU.

Maze-solving Cellular Automaton

There’s a dead simple 2-state cellular automaton that can solve any perfect maze of arbitrary dimension. Each cell is either OPEN or a WALL, only 4-connected neighbors are considered, and there’s only one rule: if an OPEN cell has only one OPEN neighbor, it becomes a WALL.

On each step the dead ends collapse towards the solution. In the above GIF, in order to keep the start and finish from collapsing, I’ve added a third state (red) that holds them open. On a GPU, you’d have to do as many draws as the length of the longest dead end.

A perfect maze is a maze where there is exactly one solution. This technique doesn’t work for mazes with multiple solutions, loops, or open spaces. The extra solutions won’t collapse into one, let alone the shortest one.

To fix this we need a more advanced cellular automaton.

Path-solving Cellular Automaton

I came up with a 12-state cellular automaton that can not only solve mazes, but will specifically find the shortest path. Like above, it only considers 4-connected neighbors.

If we wanted to consider 8-connected neighbors, everything would be the same, but it would require 20 states (n, ne, e, se, s, sw, w, nw) instead of 12. The rules are still pretty simple.

This can be generalized for cellular grids of any arbitrary dimension, and it could even run on a GPU for higher dimensions, limited primarily by the number of texture uniform bindings (2D needs 1 texture binding, 3D needs 2 texture bindings, 4D needs 8 texture bindings … I think). But if you need to find the shortest path along a five-dimensional grid, I’d like to know why!

So what does it look like?

FLOW cells flood the entire maze. Branches of the maze are search in parallel as they’re discovered. As soon as an END cell is touched, a ROUTE is traced backwards along the flow to the BEGIN cell. It requires double the number of steps as the length of the shortest path.

Note that the FLOW cell keep flooding the maze even after the END was found. It’s a cellular automaton, so there’s no way to communicate to these other cells that the solution was discovered. However, when running on a GPU this wouldn’t matter anyway. There’s no bailing out early before all the fragment shaders have run.

What’s great about this is that we’re not limited to mazes whatsoever. Here’s a path through a few connected rooms with open space.

Maze Types

The worst-case solution is the longest possible shortest path. There’s only one frontier and running the entire automaton to push it forward by one cell is inefficient, even for a GPU.

The way a maze is generated plays a large role in how quickly the cellular automaton can solve it. A common maze generation algorithm is a random depth-first search (DFS). The entire maze starts out entirely walled in and the algorithm wanders around at random plowing down walls, but never breaking into open space. When it comes to a dead end, it unwinds looking for new walls to knock down. This methods tends towards long, winding paths with a low branching factor.

The mazes you see in the demo are Kruskal’s algorithm mazes. Walls are knocked out at random anywhere in the maze, without breaking the perfect maze rule. It has a much higher branching factor and makes for a much more interesting demo.

Skipping the Route Step

On my computers, with a 1023x1023 Kruskal maze it’s about an order of magnitude slower (see update below) than A* (rot.js’s version) for the same maze. Not very impressive! I believe this gap will close with time, as GPUs become parallel faster than CPUs get faster. However, there’s something important to consider: it’s not only solving the shortest path between source and goal, it’s finding the shortest path between the source and any other point. At its core it’s a breadth-first grid search.

Update: One day after writing this article I realized that glReadPixels was causing a gigantic bottlebeck. By only checking for the end conditions once every 500 iterations, this method is now equally fast as A* on modern graphics cards, despite taking up to an extra 499 iterations. In just a few more years, this technique should be faster than A*.

Really, there’s little use in ROUTE step. It’s a poor fit for the GPU. It has no use in any real application. I’m using it here mainly for demonstration purposes. If dropped, the cellular automaton would become 6 states: OPEN, WALL, and four flavors of FLOW. Seed the source point with a FLOW cell (arbitrary direction) and run the automaton until all of the OPEN cells are gone.

Detecting End State

The ROUTE cells do have a useful purpose, though. How do we know when we’re done? We can poll the BEGIN cell to check for when it becomes a ROUTE cell. Then we know we’ve found the solution. This doesn’t necessarily mean all of the FLOW cells have finished propagating, though, especially in the case of a DFS-maze.

In a CPU-based solution, I’d keep a counter and increment it every time an OPEN cell changes state. The the counter doesn’t change after an iteration, I’m done. OpenGL 4.2 introduces an atomic counter that could serve this role, but this isn’t available in OpenGL ES / WebGL. The only thing left to do is use glReadPixels to pull down the entire thing and check for end state on the CPU.

The original 2-state automaton above also suffers from this problem.

Encoding Cell State

Cells are stored per pixel in a GPU texture. I spent quite some time trying to brainstorm a clever way to encode the twelve cell states into a vec4 color. Perhaps there’s some way to exploit blending to update cell states, or make use of some other kind of built-in pixel math. I couldn’t think of anything better than a straight-forward encoding of 0 to 11 into a single color channel (red in my case).

int state(vec2 offset) {
    vec2 coord = (gl_FragCoord.xy + offset) / scale;
    vec4 color = texture2D(maze, coord);
    return int(color.r * 11.0 + 0.5);

This leaves three untouched channels for other useful information. I experimented (uncommitted) with writing distance in the green channel. When an OPEN cell becomes a FLOW cell, it adds 1 to its adjacent FLOW cell distance. I imagine this could be really useful in a real application: put your map on the GPU, run the cellular automaton a sufficient number of times, pull the map back off (glReadPixels), and for every point you know both the path and total distance to the source point.


As mentioned above, I ran the GPU maze-solver against A* to test its performance. I didn’t yet try running it against Dijkstra’s algorithm on a CPU over the entire grid (one source, many destinations). If I had to guess, I’d bet the GPU would come out on top for grids with a high branching factor (open spaces, etc.) so that its parallelism is most effectively exploited, but Dijkstra’s algorithm would win in all other cases.

Overall this is more of a proof of concept than a practical application. It’s proof that we can trick OpenGL into solving mazes for us!

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Chris Wellons

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