Scientific Computing with Julia
Tutorials
- Julia Documentation
- Julia By Example by Samuel Colvin
- The Julia Express by Bogumił Kamiński
- Learn Julia the Hard Way by Chris von Csefalvay
- Introduction to Julia for scientific Computing by David Sanders at JuliaCon 2015
- Intermediate Level Julia by David Sanders at JuliaCon 2016
- Introduction to Writing High Performance Julia by Arch D. Robison at JuliaCon 2016
Useful Packages
- StaticArrays.jl: Statically Sized Arrays
- DistributedArrays.jl: Distributed Arrays
- ForwardDiff.jl: Forward Mode Automatic Differentiation (Tutorial)
- SymPy.jl: Julia Interface to SymPy (Tutorial)
- Plots.jl: Plotting and Visualizations with various Backends (Tutorial)
- PyPlot.jl: Julia Interface to matplotlib
- Gadfly.jl: Plotting and Visualization System based on The Grammar of Graphics (Tutorial)
- PETSc.jl: Julia Wrappers for PETSc
- HDF5.jl: Efficient Storage of Scientific Data in the HDF5 Format
Other Useful References
- Metaprogramming by Andrew Collier
- Symbolic Differentiation in Julia by John Myles White
- Inheriting type behavior in Julia by Christian Groll
- Automatic differentiation for machine learning in Julia