From Julia to Rust: a differentiable tensor stack for scientific computing in the agentic AI era
Summary
This article announces tenferro-rs, a Rust-native differentiable tensor stack for scientific computing, integrating eager autodiff, traced transforms, and CPU/CUDA backends. It explains the motivation for moving from Julia to Rust, the architectural decisions (separating operation families and autodiff, explicit backends, and column-major storage), and the ability to reuse traced graphs with runtime-shaped data. It also covers correctness checks, benchmarks, and open-source availability with links to docs and repositories.