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

Key Points:

• The "award acceptance lecture" genre is often banal, but Turing Award lectures by top computer scientists have inspired new paradigms, warned of security risks, and emphasized the power of notation in programming.
• Kenneth Iverson’s 1979 Turing Award lecture highlighted how mathematical notation can unlock new insights, exemplified by his creation of APL, a concise programming language that fused mathematical and programming languages.
• The modern scientific computing field faces a "two-language problem," where Python is used for prototyping due to its ease but is too slow for performance-critical tasks, which require faster languages like C++ or Rust.
• Julia, created in 2012 by four computer scientists, aims to combine Python’s ease with C’s speed, attracting an academic community and offering significant speedups, but it remains a niche language without widespread adoption by Big Tech.
• Despite its strengths, Julia has not replaced Python due to Python’s robust ecosystem and corporate backing for other languages; the two-language problem persists across software domains, reflecting inherent trade-offs between usability and performance.