Fidget

Fidget is experimental infrastructure for complex closed-form implicit surfaces.

At the moment, it is quietly public: it's available on Github and published to crates.io, but I'd appreciate if you refrain from posting it to Hacker News / Twitter / etc; I'm planning to write an overview blog post and put together a few demo applications before making a larger announcement. If you have an overwhelming urge to talk about it, feel free to reach out directly!

The library contains a variety of data structures and algorithms, e.g.

Manipulation and deduplication of math expressions
Conversion from graphs into straight-line code ("tapes") for evaluation
Tape simplification, based on interval evaluation results
A very fast JIT compiler, with hand-written aarch64 and x86_64 routines for
- Point-wise evaluation (f32)
- Interval evaluation ([lower, upper])
- SIMD evaluation (f32 x 4 on ARM, f32 x 8 on x86)
- Gradient evaluation (partial derivatives with respect to x, y, and z)
Bitmap rendering of implicit surfaces in 2D (with a variety of rendering modes) and 3D (producing heightmaps and normals)
Meshing (using our own implementation of the Manifold Dual Contouring algorithm)

If this all sounds oddly familiar, it's because you've read Massively Parallel Rendering of Complex Closed-Form Implicit Surfaces. Fidget includes all of the building blocks from that paper, but with an emphasis on (native) evaluation on the CPU, rather than (interpreted) evaluation on the GPU.

The library has extensive documentation, including a high-level overview of the APIs in the crate-level docs; this is a great place to get started!

At the moment, it has strong Lego-kit-without-a-manual energy: there are lots of functions that are individually documented, but putting them together into something useful is left as an exercise to the reader. There may also be some missing pieces, and the API seams may not be in the right places; if you're doing serious work with the library, expect to fork it and make local modifications.

Issues and PRs are welcome, although I'm unlikely to merge anything which adds substantial maintenance burden. This is a personal-scale experimental project, so adjust your expectations accordingly.

Demos

The demos folder contains several demo tools and applications built using the Fidget crate, ranging from CLI to GUI to web app.

Support matrix

At the moment, Fidget supports a limited number of platforms:

Platform	JIT support	CI	Support
`aarch64-apple-darwin`	Yes	✅ Tested	⭐️ Tier 0
`x86_64-unknown-linux-gnu`	Yes	✅ Tested	🥇 Tier 1
`x86_64-pc-windows-msvc`	Yes	✅ Tested	🥈 Tier 2
`aarch64-unknown-linux-gnu`	Yes	⚠️ Checked	🥇 Tier 1
`aarch64-pc-windows-msvc`	Yes	⚠️ Checked	🥉 Tier 3
`wasm32-unknown-unknown`	No	⚠️ Checked	🥇 Tier 1

Explanation of keys

CI	Description
✅ Tested	`cargo test` is run for the given target
⚠️ Checked	`cargo check` is run for the given target

Tier	Description
⭐️ Tier 0	A maintainer uses this platform as their daily driver
🥇 Tier 1	A maintainer has access to this platform
🥈 Tier 2	A maintainer does not have access to this platform, but it is tested in CI
🥉 Tier 3	A maintainer does not have access to this platform, and it is not tested in CI

Support tiers represent whether maintainers will be able to help with platform-specific bugs; for example, if you discover an aarch64-pc-windows-msvc-specific issue, expect to do most of the heavy lifting yourself.

CPU requirements

aarch64 platforms require NEON instructions and x86_64 platforms require AVX2 support; both of these extensions are nearly a decade old and should be widespread.

Disabling the jit feature allows for cross-platform rendering, using an interpreter rather than JIT compilation. This is mandatory for the wasm32-unknown-unknown target, which cannot generate "native" code.

Similar projects

Fidget overlaps with various projects in the implicit modeling space:

*written by the same author

(the MPR paper also cites many references to related academic work)

Compared to these projects, Fidget is unique in having a native JIT and using that JIT while performing tape simplification. Situating it among projects by the same author – which all use roughly the same rendering strategies – it looks something like this:

	CPU	GPU
Interpreter	`libfive`, Fidget	MPR
JIT	Fidget	(please give me APIs to do this)

Fidget's native JIT makes it blazing fast. For example, here are rough benchmarks rasterizing this model across three different implementations:

Size	`libfive`	MPR	Fidget (VM)	Fidget (JIT)
1024³	66.8 ms	22.6 ms	61.7 ms	23.6 ms
1536³	127 ms	39.3 ms	112 ms	45.4 ms
2048³	211 ms	60.6 ms	184 ms	77.4 ms

libfive and Fidget are running on an M1 Max CPU; MPR is running on a GTX 1080 Ti GPU. We see that Fidget's interpreter is slightly better than libfive, and Fidget's JIT is nearly competitive with the GPU-based MPR.

Fidget is missing a bunch of features that are found in more mature projects. For example, it only includes a debug GUI, and its meshing is much less battle-tested than libfive.

mkeeter/fidget