Accelerating Differential Equations In R and Python
summary
Claims real speedups: the Julia-backed diffeqpy and R wrapper diffeqr give concrete benchmarks (50–100× vs SciPy, 350× vs deSolve, a 175× Pfizer case) and include GPU and MPI support, symbolic optimizations via ModelingToolkit.jl, and a compiler pipeline that turns user functions into fast Julia code. The Python wrapper works but full-model-tokenization is incomplete so best performance currently requires sending a Julia string to eval; automated Julia installation and packaging into system images are planned but not yet finished.
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chapters (8)
- 0:00Introduction
- 2:32Julia vs Python libraries
- 7:46SciML and CyIML
- 15:27Julia solver performance
- 22:22Bridging Julia and Python/R
- 26:50Accelerating R with DiffEqQR
- 30:19Python integration limits
- 33:08Future improvements
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