Jonathan Diegelman - Modeling Spacecraft Separation Dynamics in Julia
Why Hacker News recommends it
Cited again and again as the concrete case study behind the claim that NASA got a 15,000x speedup moving from Simulink to Julia.
What it is
A JuliaCon talk by a NASA Launch Services Program engineer on modeling spacecraft separation dynamics in Julia. He describes replacing a Simulink-based workflow for checking launch vehicle separation requirements with Julia and ModelingToolkit, with large speedups and much shorter code.
Recommended by 2 HN commenters across 2 different months, 2021 to 2023.
What Hacker News said
Recommends NicolasL-S · 2022-09 · on “From Common Lisp to Julia”
You should check out this case study of migrating Simulink to Julia at NASA: [this video] From Simulink to Julia, his code went from 1000 lines to 50 lines and ran 15,000x faster.
Recommends eggy · 2023-01 · on “GNU Octave”
<<was given at a SIAM conference and the recording can be found here: [this video] >> At 14:00, the interactive slider exploration due to the speed is amazing. Over 500 Monte Carlo simulations in less than 60ms.
Cites snicker7 · 2021-07 · on “Julia Computing raises $24M Series A”
For satellite seperation dynamics modeling, NASA switched from MATLAB (Simulink) to Julia and got a 15,000x performance boost. [this video]
Cites amkkma · 2021-08 · on “Growing open source from Torch to PyTorch”
Sure! NASA: [this video] Moderna: [LINK] [LINK] There are many many more. These unique and sought after capability are what got Julia Computing its 24 mil series A ( [LINK] )
Cites snicker7 · 2021-09 · on “Fortran Web Framework”
NASA switched from SIMULINK to ModelingToolkit (Julia) for modeling spacecraft dynamics [0]. They found that the latter was much easier and gave a 15,000x (!!!) performance improvement. [0]: [this video]
Cites ChrisRackauckas · 2021-10 · on “Composability in Julia: Implementing Deep Equilibrium Models via Neural ODEs”
Lots to say here. First of all, the community growth has been pretty tremendous and I couldn't really ask for more. We're seeing tens of thousands of visitors to the documentation of various packages, and have some high profile users. For example, NASA showing a 15,000x acceleration ( [this video] ) and the Head of Clinical Pharmacology at Moderna saying SciML-based Pumas "has emerged as our 'go-to' tool for most of our analyses in recent months" in 2020 (see [LINK] ). We try to keep a showcase ( [LINK] ) but at this point it's hard to stay on top of the growth. I think anyone would be excited to see an open source side project reach that level of use. Since we tend to focus on core numerical iss
Cites ChrisRackauckas · 2023-01 · on “GNU Octave”
ModelingToolkit.jl, a comparison is given here: [LINK] . A showcase from one of the library's users, demonstrating a 15,000x acceleration, was given at a SIAM conference and the recording can be found here: [this video]
Cites ChrisRackauckas · 2023-05 · on “Can Fortran survive another 15 years?”
> Definitely 15000 times faster than MATLAB as claimed by JuliaComputing can be achieved with such seemingly concrete and realistic benchmarks. I believe you're talking about NASA Launch Services engineers claiming Julia's ModelingToolkit simulations outperformed Simulink by 15,000x? That claim was of course not made by Julia Computing or anyone affiliated by Julia Computing, which is pretty clear because the person who makes the claim very clearly describes his affiliation at the beginning of the video. The source is here: [this video] , at 12:55. You did watch the whole video to understand the application and the caveats etc. instead of just reading the headline and immediately coming to a conc