Help:Julia

Julia is a high-level dynamic programming language designed to address the needs of high-performance numerical analysis and computational science.

Matlab code can be directly translated into julia, and tools from R and Python are included in the julia libraries, and there are interfaces for using Julia packages from R and Python. Julia can directly call C libraries as well. Julia is compiled using LLVM as a back end, and resulting code rivals or is faster than C.

Vendor homepage

https://julialang.org/

Julia Package Indices

https://julialang.org/packages/

https://juliahub.com/ - Searchable listing of all registered open source packages

https://juliaobserver.com/ - see what packages are popular and/or trending, navigate by package categories.

https://github.com/svaksha/Julia.jl a manually curated taxonomy of Julia packages

MIT Julia Lab

https://julia.mit.edu/

Software availability

Windows, Unix, macOS, Linux

ask if you want it installed on a server

JuliaPro is a commercial "batteries included" Julia distribution for Windows, Mac, and Linux. Similar to Anaconda for Python.

It looks like JuliaPro has been discontinued

Other related software

matlab, R, python, atom

command to type to run

julia

Interesting Packages and Suites

This is a selection of particularly interesting Julia tools that are either relevant to general scientific computing or directly applicable to known research happening at UCF. This is by no means comprehensive, as the Julia ecosystem is growing by the day.

• SciML - An Open Source Software Organization for Scientific Machine Learning, blending ML with Differential Equations (See this blog post for an in depth look at what SciML does, along with some comparative benchmarks)
  • ModelingToolkit.jl - A modeling framework for high-performance symbolic-numeric computation in scientific computing and scientific machine learning.
  • DifferentialEquations.jl - a suite for numerically solving differential equations written in Julia and available for use in Julia, Python, and R.
  • NBodySimulator.jl - A simulator for N-body problems, including astrophysical and molecular dynamics
• FluxML - Native Julia machine learning and differentiable programming packages
  • Flux.jl - 100% Julia Machine Learning with support for GPUs and other accelerators
  • Zygote.jl - Automatic Differentiation in Julia
• CUDA:
  • CUDA.jl - CUDA programming for NVIDIA GPUs in Julia

External Links

• Why we created julia
• Julia for MATLAB Users

• matlab to julia translator

• FluxML machine learning
• https://github.com/kpamnany/Gasp.jl
• https://github.com/IntelLabs/ParallelAccelerator.jl
• https://github.com/IntelLabs/Latte.jl
• FEA solver with Abaqus?
• Bioinformatics optimizations?
• JuliaML Julia packages for Machine Learning
• JuliaAcademy Online Julia training courses
• Julia Computing, Inc. Commercial Julia products and Support
• https://www.stat.wisc.edu/~bates/JuliaForRProgrammers.pdf