Rust, For Science!
summary
Rust's fit for scientific computing gets concrete treatment: Rayon parallelism example and easy cargo integration, MPI bindings and C/Fortran FFI for HPC and libraries like OpenCL/ArrayFire, plus built‑in testing, Clippy and benchmarking. Demo compiles to WebAssembly for a double‑pendulum but the live demo fails, and evcxr Jupyter support exists but is flaky on Mac/Windows and unstable. Clear tradeoffs: speed and safety versus a patchy ecosystem and missing convenience libraries.
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chapters (6)
- 0:00Start
- 4:41Easy Unit Tests
- 5:10Writing Better Code
- 6:06Example: Numerical Integration
- 7:27Example: MPI
- 12:01Thank you!
