Considering Rust for scientific software
summary
Argues that Rust yields fast, correct scientific software by combining zero-cost performance with safety features: borrow checker, explicit unsafe blocks, no implicit primitive conversions, bounds checks by default, careful floating-point handling, and guaranteed no data races in safe Rust. Notes practical tooling: Cargo, integrated testing and doc-tests, convenient debug printing, and Python interop examples. Cautions about a younger ecosystem, steeper learning curve, and a single dominant compiler, but shows these trade-offs enable fewer runtime bugs and easier maintenance.
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chapters (20)
- 0:00Intro
- 0:18Welcome
- 0:43Who am I
- 1:10Importance of correctness
- 2:26Developers have other jobs
- 4:53Python
- 5:49C
- 7:47Reasons not to use Rust
- 10:17Reasons to use Rust
- 12:27Rust features
- 13:42Explicit conversion
- 14:39Safe defaults
- 16:02Unsafe blocks
- 18:08Floating point numbers
- 19:30Sorting floating point numbers
- 20:35Prototyping in Rust
- 21:47Testing in Rust
- 22:42Documentation tests
- 24:01C Core Guidelines
- 24:29What sets Rust apart
