Why Rust fails hard at scientific computing

[kryptan]

There are two kinds of scientific programmers, those who use scientific libraries and those who write them. Users of scientific libraries are usually scientists, not professional programmers, and therefore need a simple language. Simple implies interpreted, which implies slow, but this can be mitigated by having the core algorithms written in a lower-level language and exposed as a vectorized library in the higher-level language. Numpy and Scipy do this for Python. R and Matlab have a similar design built in.

Author of Rust has an interesting article about how Julia solves this dichotomy and gives you a language which is suitable for both writing high-perfomance libraries and conveniently using them.

[system]

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