ComponentArrays.jl
DifferentialEquations.jl
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| ComponentArrays.jl | DifferentialEquations.jl | |
|---|---|---|
| 1 | 6 | |
| 276 | 2,756 | |
| - | 1.6% | |
| 7.0 | 7.2 | |
| 4 days ago | 20 days ago | |
| Julia | Julia | |
| MIT License | GNU General Public License v3.0 or later |
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For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
ComponentArrays.jl
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Recursion absolutely necessary for distributed computing?
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.
DifferentialEquations.jl
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Startups are building with the Julia Programming Language
This lists some of its unique abilities:
https://docs.sciml.ai/DiffEqDocs/stable/
The routines are sufficiently generic, with regard to Julia’s type system, to allow the solvers to automatically compose with other packages and to seamlessly use types other than Numbers. For example, instead of handling just functions Number→Number, you can define your ODE in terms of quantities with physical dimensions, uncertainties, quaternions, etc., and it will just work (for example, propagating uncertainties correctly to the solution¹). Recent developments involve research into the automated selection of solution routines based on the properties of the ODE, something that seems really next-level to me.
[1] https://lwn.net/Articles/834571/
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From Common Lisp to Julia
https://github.com/SciML/DifferentialEquations.jl/issues/786. As you could see from the tweet, it's now at 0.1 seconds. That has been within one year.
Also, if you take a look at a tutorial, say the tutorial video from 2018,
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When is julia getting proper precompilation?
It's not faith, and it's not all from Julia itself. https://github.com/SciML/DifferentialEquations.jl/issues/785 should reduce compile times of what OP mentioned for example.
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Julia 1.7 has been released
Let's even put raw numbers to it. DifferentialEquations.jl usage has seen compile times drop from 22 seconds to 3 seconds over the last few months.
https://github.com/SciML/DifferentialEquations.jl/issues/786
- Suggest me a Good library for scientific computing in Julia with good support for multi-core CPUs and GPUs.
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DifferentialEquations compilation issue in Julia 1.6
https://github.com/SciML/DifferentialEquations.jl/issues/737 double posted, with the answer here. Please don't do that.
What are some alternatives?
DiffEqBase.jl - The lightweight Base library for shared types and functionality for defining differential equation and scientific machine learning (SciML) problems
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
JuMP.jl - Modeling language for Mathematical Optimization (linear, mixed-integer, conic, semidefinite, nonlinear)
diffeqpy - Solving differential equations in Python using DifferentialEquations.jl and the SciML Scientific Machine Learning organization
RayTracer.jl - Differentiable RayTracing in Julia
Gridap.jl - Grid-based approximation of partial differential equations in Julia
ApproxFun.jl - Julia package for function approximation
ControlSystems.jl - A Control Systems Toolbox for Julia
DSGE.jl - Solve and estimate Dynamic Stochastic General Equilibrium models (including the New York Fed DSGE)
FFTW.jl - Julia bindings to the FFTW library for fast Fourier transforms