Kotlin for Science
summary
Kotlin appears as a working option for particle physics: a partial Java-to-Kotlin migration on a 100,000-line experiment improves maintainability while preserving interoperability; Monte‑Carlo muon simulations and a JavaFX visualization get rewritten more concisely; a lightweight plotly-kotlin wrapper and kmath provide numeric/front-end bridges; coroutines implement tree‑growth parallelism with reproducible seeds; Kotlin/Native serves as a JNI-friendly bridge, while measurable issues remain (boxing overhead and limited native/visualization libraries).
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chapters (18)
- 0:00Intro
- 1:03LET'S TALK ABOUT... NEUTRINOS
- 1:47A lot of neutrinos around the room
- 4:22Why scientific programming is special?
- 6:10A language for scientific programming should be
- 7:26What's wrong with C++
- 9:24What's wrong with Python - Superior glue language has bindings for everything
- 11:45What's wrong with...
- 15:39Better Java (Muon monitor)
- 19:02Generic numeric operations
- 25:03Extensions
- 26:12Scoped mathematics
- 30:50Random branching
- 34:15DAQ and Slow control
- 35:58Visualization problem
- 40:35A bridge to the native world
- 41:26Other examples
- 41:55Boxing... boxing...
