Scientific Computing in Rust Monthly #16
Welcome to this month's issue of Scientific Computing in Rust Monthly.
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Crate of the month: rstest
rstest is a fixture based test framework for Rust. It uses procedural macros to allow writing of fixtures on table-based tests. This can be very useful, when you want to, e.g., test multiple values without rewriting the tests.
New releases
Cherry
Cherry is a sequential ray tracer for optical systems design that runs entirely in the browser and is written in Rust. An updated version of Cherry was recently published in which the original Javascript user interface was replaced with egui, an immediate mode GUI for Rust with WASM support. To try Cherry, click the link above and enjoy designing!
complex-bessel 0.1.0
A pure Rust implementation of the Amos (TOMS 644) algorithm for computing Bessel, Hankel, and Airy functions of complex argument and real order. No Fortran/C FFI dependencies required.
la-stack 0.3.0
la-stack is a fast, stack-allocated linear algebra package for fixed dimensions in Rust.
Recent changes for v0.3.0 can be found here.
polars v0.53.0
Polars is an analytical query engine written for DataFrames. It is designed to be fast, easy to use and expressive. Highlights of this release include the addition of extension types. A full list of changes can be found here.
vforce 1.0.0
Vforce provides safe, no_std Rust bindings for the VForce family of hardware-accelerated vectorized math functions in the Accelerate framework on MacOS. It is generic over single and double-precision floats, and automatically chunks over very large arrays. Useful for speeding up transcendental functions on Apple hardware when working with 1-d arrays.
zarrs v0.23.6
zarrs is a Rust library for the Zarr storage format for multidimensional arrays and metadata.
Recent changes since v0.23.0 (Feb 1, 2026), can be found here.
Publications
N. R. Posner, When Vectorized Arrays Aren't Enough
An exploration of Rust optimizations made to a Python astrophysics simulation, from bytecode to assembly. Provides examples and builds intuition on how and when to deploy lower-level solutions to avoid NumPy's intermediate allocations.
