A fresh approach to Numerical Computing
Zachary Sunberg, Assistant Professor, AERO
Survey
Scientific computing is pretty good these days
Fast Development
Fast Execution
2011
2013
We love [Matlab, Lisp, Python, Ruby, Perl, Mathematica, and C]; they are wonderful and powerful. For the work we do — scientific computing, machine learning, data mining, large-scale linear algebra, distributed and parallel computing — each one is perfect for some aspects of the work and terrible for others. Each one is a trade-off.
We are greedy: we want more.
2012
Also ~2011: Improving TCAS
Julia solves the two language problem!
- as easy to read, write, maintain, and collaborate with as Python or MATLAB
- as fast as C
EASIER
FASTER
Be skeptical! Find out for yourself!
Others' perspective: https://web.mit.edu/18.06/www/Spring17/Julia-intro.pdf
Julia - Speed
Celeste Project
1.54 Petaflops
JuliaCon 2023 (EHT Tools in C++)
Not because Julia is somehow faster, but because it was easier to dev better algs
Julia - Speed
Julia - Speed
How does it work?
Speed: Just-in-time LLVM Compilation
If Julia is so great, why haven't I heard of it?
015 Julia
By Zachary Sunberg
