Cocos2d VS LiquidFun

As others have pointed out, the biggest difference you're seeing is likely due to the _version_ of OpenGL (and hence GLSL) version. That said, there are still important differences. I'd recommend looking at a comparison between the same shader in a project that supports both OpenGL & OpenGLES. For example, here's a shader from cocos2d-x https://github.com/cocos2d/cocos2d-x/blob/v4/cocos/renderer/shaders/positionColor.vert. Note the only difference in this case is the additional precision qualifier (lowp) for v_fragmentColor. Note too how cocos uses preprocessor macros to handle this, so they don't have to maintain separate shader sources. Depending on your goals, you might be interested in tools like Nvidia cg or nvFX that allow for creating shaders in a dialect agnostic way, but ymmv.

Speaking about markdown files on GitHub, I know that there are some repositories that have very stunning README markdown files, such as this one for Cocos2d-x, whcih includes images in conjunction with text. I might do the same for my repository as well. Considering VS Code have an extension for .md files, this might come in handy when I consider using VS Code to contribute to my game projects.

Some years ago I did a project with LiquidFun; https://google.github.io/liquidfun/

It was an interactive art thing for kids; had a TV with a maze of transparent PVC pipes mounted on the front with valves (rotary encoders inside). The kids would open and close the valves, and watch the flow of water be redirected on the TV behind.

Author here. This demo showcases liquidfun-wasm[0], my effort to revive liquidfun[1] (a fork which adds fluid simulation and soft-body physics to Box2D[2]).

to make liquidfun-wasm, I repurposed my existing box2d-wasm[3] and pointed it at a different release of Box2D — a commit obtained by rebasing liquidfun over 7 years of upstream Box2D changes[4]. the end result is that liquidfun is now distributed in WebAssembly and with TypeScript typings for the first time. The TypeScript typings are generated from WebIDL bindings via my webidl-to-ts[5] compiler.

this demo in particular aims to bring to the Web the shaders from the liquidfun EyeCandy demo[6], and show how fast JS can run if you avoid incurring the garbage collector (the main loop tries not to allocate objects). the demo repurposes gravity and drag calculations that I'd used previously in my Lunar Survey experiment[7] (a Mario Galaxy homage).

[0] https://github.com/Birch-san/box2d-wasm/releases/tag/liquidf...

[1] http://google.github.io/liquidfun/

[2] https://github.com/erincatto/box2d

[3] https://github.com/Birch-san/box2d-wasm

[4] https://github.com/Birch-san/box2d-wasm/releases/tag/v4.0.0-...

[5] https://github.com/Birch-san/box2d-wasm/tree/master/webidl-t...

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

[7] https://birchlabs.co.uk/lunar-survey/

Liquidfun [0] diverged from Box2D at v2.3.0, circa November 2013.

liquidfun-wasm [1] is my effort to rebase the liquidfun contributions onto latest Box2D, v2.4.1 (October 2020), and to distribute it in WebAssembly with TypeScript typings.

this work is detailed in liquidfun-wasm's first release notes. [2]

I've also enabled WASM SIMD acceleration (via LLVM's autovectorizer) on supported devices. Haven't yet measured what performance difference this makes.

[0] http://google.github.io/liquidfun/

yeah, I've played around with a few approaches for running the timestep and for some reason I don't feel like I get the same results as liquidfun.js.

their loop [0] is pretty simple; it's scheduled by `requestAnimationFrame`, advances time by 1/60th of a second, and runs their default of 3 particle iterations. it completes the physics simulation within 3.9–5.5ms, which is easily in time for the 16ms deadline. the rendering is WebGL, which I assume fits easily into that 16ms budget too.

my loop [1] is more complicated; I don't hardcode the timestep to 1/60 seconds, because requestAnimationFrame may be scheduled less frequently than that. so instead I advance time by the time elasped since I was last scheduled. hm, I think there's a mistake there — `lastMs = nowMs` is probably on the wrong side of the physics calculation.

there's an additional technique I use: I put a `Math.min()` over the simulation interval, so that I don't attempt to simulate more than 20ms (this can happen if you get scheduled infrequently due to hot CPU or backgrounding the app) — simulating too much time will make us fail our frame deadline anyway.

furthermore, if we are calculating more than 1/60th of a second, I employ more particle iterations (i.e. 3 particle iterations for every 1/60th of a second that passes). this gave me good results, but turns out it is based on incorrect assumptions (iterations are unrelated to timestep)[3]. moreover, I may be making mistakes in my decision of whether to round this fraction up/down.

if too few particle iterations for a timestep: the particles will bounce. if too many: the particles will look too incompressible[4]. I think that's the "solid-like" structure you're describing.

the main reason I complicated this is because the last one I did[5] made me feel motion-sick. I think if "every 1/60th, or 1/30th, or 1/20th of a second: you simulate a 1/60th of a second of time": the result (if you're not scheduled consistently) is that the world speed keeps changing. I think liquidfun.js's approach should be vulnerable to this, but for some reason it looks fine to me. maybe they get scheduled more consistently than me (even though by my measurements, my physics runs slightly faster, so should be able to achieve similar results).

I think I need to remind myself of what happens if I program the timestep in the simple way that liquidfun.js did. will try that out at some point.

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

[1] https://github.com/Birch-san/liquidfun-play-2/blob/master/sr...

[2] https://github.com/Birch-san/liquidfun-play-2/blob/master/sr...

[3] http://google.github.io/liquidfun/Programmers-Guide/html/md_...

[4] http://google.github.io/liquidfun/Programmers-Guide/html/md_...

[5] https://birchlabs.co.uk/box2d-wasm-liquidfun/

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...

It should be possible to produce simulations like the ones they produced in JS: http://google.github.io/liquidfun/

He was involved in an open-source project titled LiquidFun, which was released late in 2013 and unfortunately only went through 3 versions, ending developmentin mid 2014. https://github.com/google/liquidfun/releases