box2d.ts VS box2d-wasm

yes, I compiled with -msimd128 to enable LLVM's auto-vectorization. I distribute both SIMD and non-SIMD, and the entrypoint picks whichever distribution your browser supports. for box2d-wasm, SIMD acceleration resulted in a 0.6–0.9% performance boost [0] when simulating a pyramid of boxes.

liquidfun-wasm is a fork with additional algorithms for performantly simulating particles. I have not yet built a benchmark to measure the particle code, but do intend to. I am optimistic that liquidfun's particle code could auto-vectorize better than the general Box2D code.

the Google engineers considered how to take advantage of SIMD, to the extent that they even ship a NEON SIMD algorithm[1]. I don't believe my compiler config will use that NEON algorithm (and will instead fallback to the general algorithm [2]). that's probably not a missed opportunity; many NEON features are not supported[3]. but since the engineers were thinking about SIMD, hopefully the non-NEON algorithm will try to make good use of the CPU and memory layout too, and auto-vectorize well.

[0] https://github.com/Birch-san/box2d.ts/pull/1

[1] https://github.com/google/liquidfun/blob/master/liquidfun/Bo...

[2] https://github.com/google/liquidfun/blob/master/liquidfun/Bo...

[3] https://emscripten.org/docs/porting/simd.html#compiling-simd...

https://github.com/Birch-san/box2d-wasm.) Godot uses box2d, too, so that would be convenient, if I switch to godot, but only if it is worth the performance improvement, which it currently does not seem to be. Maybe next year.

Following the article, you build a simple 2D physic simulation (only for balls). Did by chance anyone expand on that to include boxes, or know of a different approach to build a physic engine in WebGPU?

I experiemented a bit with it and imolemented raycasting, but it is really not trivial getting the data in and out. (Limiting it to boxes and circles would satisfy my use case and seems doable, but getting polygons would be very hard, as then you have a dynamic size of their edges to account for and that gives me headache)

3D physic engine on the GPU would be the obvious dream goal to get maximum performance, but that is really not an easy thing to do.

Right now I am using a Box2D for wasm and it has good performance, but it could be better.

https://github.com/Birch-san/box2d-wasm

The main problem with all this is the overhead of getting data into the gpu and back. Once it is on the gpu it is amazingly fast. But the back and forth can really make your framerates drop - so to make it worth it, most of the simulation data has to remain on the gpu and you only put small chanks of data that have changed in and out. And ideally render it all on the gpu in the next step.

(The performance bottleneck of this simulation is exactly that, it gets simulated on the gpu, then retrieved and drawn with the normal canvasAPI which is slow)

box2dwasm - an old, still maintained C++ library compiled to WASM. The documentation is lacking and developer experience seems poor.

network inspector says 2.1MB. but that's dominated by a 1.3MB image.

the main assets of the library are:

- Box2D.simd.js (422kB)

- Box2D.simd.wasm (266 kB)

a minimal demo that uses the library can be created in just a few kB:

https://github.com/Birch-san/box2d-wasm/tree/master/demo/mod...

A couple weeks ago I ported liquidfun to TypeScript + WebAssembly: https://github.com/Birch-san/box2d-wasm/releases/tag/v4.0.0-liquidfun.0