Python Is So Slow. Can Julia Solve the Two-Language Problem?

8 hours ago

Award acceptance lectures by Turing Award-winning computer scientists often stand out as influential exceptions to the banality of the genre.
Examples include John Backus's 1977 lecture inspiring functional programming, Ken Thompson's 1984 warning about compiler backdoors, and Edsger Dijkstra's 1972 call for humility.
Kenneth Iverson's 1979 lecture 'Notation as a Tool of Thought' argued that mathematical notation fosters discovery, exemplified by APL, a programming language designed to fuse mathematical and programming languages.
The two-language problem in scientific computing today involves prototyping in slow Python and rewriting performance-critical parts in faster languages like C++ or Rust.
Julia, created in 2012, aims to be a single language as ergonomic as Python and as fast as C, addressing this problem.
Julia has gained a sober, academic community and offers significant speedups (e.g., 10x to 1000x faster than Python) but remains niche due to Python's robust ecosystem and lack of Big Tech adoption.
Julia is used in high-performance fields like drug discovery and machine learning at institutions like ASML, CERN, and NASA.
The two-language problem persists across domains (e.g., gaming with C++/Lua, backends with Python/Go), and it's uncertain if any language can fully solve it.
The piece is part of a series on AI-enabling languages, reflecting on the potential for future innovations akin to Iverson's insights.

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