In the world of 3D graphics, physics simulations, and scientific computing, having efficient linear algebra libraries is crucial. A new Rust library called lin-alg has emerged, offering vector, quaternion, and matrix operations for various applications including computer graphics, robotics, and scientific modeling. However, the Rust ecosystem already has several established options in this space, leading the community to question where this new library fits in.
Crowded Ecosystem with Established Players
The gamedev ecosystem in Rust appears to be primarily divided between two major linear algebra libraries: nalgebra (commonly used with the Rapier physics engine) and glam. There's also bevy_math, which many commenters noted actually re-exports glam functionality, and the older cgmath library which hasn't been updated since January 2021. This fragmentation creates challenges for developers trying to choose the right tool for their projects, especially when libraries have different design philosophies and optimization strategies.
The gamedev ecosystem appears to be split between nalgebra (for Rapier users) and glam. Where does lin-alg fit?
Implementation Differences and Performance Considerations
One of the key discussion points in the community revolves around how these libraries implement vector types internally, which directly impacts performance. According to community analysis, nalgebra uses fixed-size arrays (a Vec4 is structured as [[f32; 4]; 1]), while lin-alg appears to use individual fields (a struct with x, y, z, w fields), and glam leverages SIMD types (__m128) for some operations. These implementation differences matter significantly for performance-critical applications.
Several commenters pointed to mathbench-rs, a benchmarking project that compares various math libraries. The discussion revealed that for optimal SIMD (Single Instruction, Multiple Data) performance, many developers prefer a different approach than what glam uses - structuring vectors to process multiple problems simultaneously rather than using SIMD for a single vector operation.
Linear Algebra Libraries in Rust Ecosystem
- nalgebra: Uses fixed-size arrays, popular with Rapier physics engine
- glam: Uses SIMD types for some operations, widely used in gamedev
- bevy_math: Re-exports glam functionality
- cgmath: Older library, not updated since January 2021
- lin-alg: New library with individual field implementation, no_std support
Implementation Differences
| Library | Vector Implementation | Notes |
|---|---|---|
| nalgebra | Fixed-size arrays ([[f32; 4]; 1]) |
|
| lin-alg | Individual fields (struct with x,y,z,w) | |
| glam | SIMD types (__m128) for some types |
Key Features of lin-alg
- Vector, matrix, and quaternion operations
- f32 or f64 based types
- no_std support for embedded systems
- Computer graphics functionality available via feature flag
- Bincode binary encoding/decoding via feature flag
No_std Support and Feature Flags
One potentially novel aspect of lin-alg is its no_std support for embedded systems. However, a commenter pointed out that the library's approach to feature flags doesn't follow Rust's recommended practices. The library uses a no_std feature to disable standard library functionality, whereas the Rust community generally recommends having a std feature that conditionally enables standard library features. This approach ensures feature flags remain additive, following Rust's design principles.
Developer Motivation
While benchmarks weren't provided, community speculation about the author's motivation was confirmed by the library creator. Rather than focusing on performance advantages, the library appears to have been created due to frustrations with inconsistent quaternion operations in existing libraries. The author mentioned they use it as a low-friction way to conserve geometrical functions among multiple projects, finding it easier to modify their own codebase than go through pull request processes with other libraries.
For developers considering which linear algebra library to use in their Rust projects, the choice remains complex. While established options like nalgebra and glam have larger user bases and potentially more thorough testing, lin-alg offers an alternative that might better suit specific use cases, particularly where quaternion operations consistency is a priority or where no_std support is needed.
Reference: Vectors, quaternions, and matrices for general purposes, and computer graphics.