fractalrabbit VS ComponentArrays.jl

Compare fractalrabbit vs ComponentArrays.jl and see what are their differences.

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fractalrabbit ComponentArrays.jl
1 1
135 272
1.5% -
3.9 7.3
6 months ago 6 days ago
Java Julia
Apache License 2.0 MIT License
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fractalrabbit

Posts with mentions or reviews of fractalrabbit. We have used some of these posts to build our list of alternatives and similar projects.

We haven't tracked posts mentioning fractalrabbit yet.
Tracking mentions began in Dec 2020.

ComponentArrays.jl

Posts with mentions or reviews of ComponentArrays.jl. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-10-15.
  • Recursion absolutely necessary for distributed computing?
    3 projects | /r/Julia | 15 Oct 2021
    But for these to be as fast as say an Array when being used as the object in a differential equation solve or as the underlying construct of a nonlinear optimization, you would need the compiler to elide the struct construction which it doesn't always do. This is why the tools evolved to be around things like https://github.com/jonniedie/ComponentArrays.jl instead, where it's an Array-backed object with a higher level. Such immutable objects are used in these array-like contexts when the problems are small enough (FieldVectors or SLVector LabelledArrays.jl in DiffEq), and such applications work well in Haskell as well, but I haven't seen a compiler do well with say a 1,000 ODE model written in this style. And it's not quite an extreme case if it's what people are doing daily.

What are some alternatives?

When comparing fractalrabbit and ComponentArrays.jl you can also consider the following projects:

JuMP.jl - Modeling language for Mathematical Optimization (linear, mixed-integer, conic, semidefinite, nonlinear)

DiffEqBase.jl - The lightweight Base library for shared types and functionality for defining differential equation and scientific machine learning (SciML) problems

RayTracer.jl - Differentiable RayTracing in Julia

ModelingToolkit.jl - An acausal modeling framework for automatically parallelized scientific machine learning (SciML) in Julia. A computer algebra system for integrated symbolics for physics-informed machine learning and automated transformations of differential equations

ape-ecs - Entity-Component-System library for JavaScript.

GeoStats.jl - An extensible framework for geospatial data science and geostatistical modeling fully written in Julia

FunctionalCollections.jl - Functional and persistent data structures for Julia

ControlSystems.jl - A Control Systems Toolbox for Julia

DSGE.jl - Solve and estimate Dynamic Stochastic General Equilibrium models (including the New York Fed DSGE)

julia - The Julia Programming Language

DifferentialEquations.jl - Multi-language suite for high-performance solvers of differential equations and scientific machine learning (SciML) components. Ordinary differential equations (ODEs), stochastic differential equations (SDEs), delay differential equations (DDEs), differential-algebraic equations (DAEs), and more in Julia.

DiscretePIDs.jl - Discrete-time PID controllers in Julia