generic-cl
DifferentialEquations.jl
generic-cl | DifferentialEquations.jl | |
---|---|---|
13 | 6 | |
123 | 2,773 | |
- | 1.3% | |
0.0 | 7.2 | |
over 2 years ago | 17 days ago | |
Common Lisp | Julia | |
MIT License | GNU General Public License v3.0 or later |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
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.
generic-cl
-
Compiling a Lisp
for those wanting generic +, equality and comparison in CL, there's a nice library: https://alex-gutev.github.io/generic-cl/
-
Adding new types and operators to Lisp
If performance is not a concern, then you can create CLOS classes corresponding to vec3 or mat44, and dispatch the appropriate functions from the generic-cl project by specializing on them.
-
Emacs-like editors written in Common Lisp
> And Lisp is almost uniquely able to handle transitions to later standards as I described above. You don't actually have to forfeit backwards compatibility entirely or at all if the changes are handled by moving to a new default base package. :cl-user/:cl become :cl##-user/:cl##
Go use cl21[0] if you care for this sort of thing.
> more generic functions would open up more interesting developments later
generic-cl[1]. But in a prefix-oriented language, I just don't see this as particularly important.
> you don't necessarily want to bless a particular concurrency model
You do[2]; this is one of the notable deficiencies in the cl standard that really bites, today. It is being worked on.
0. http://cl21.org/
1. https://github.com/alex-gutev/generic-cl
2. https://www.hpl.hp.com/techreports/2004/HPL-2004-209.pdf
-
From Common Lisp to Julia
So, the article is harsh on CL: YMMV. Also, your goal may vary: I want to build and ship (web) applications, and so far Julia doesn't look attractive to me (at all). Super fast incremental development, build a standalone binary and deploy on my VPS or ship an Electron window? done. Problem(s) solved, let's focus on my app please.
The author doesn't mention a few helpful things:
- editor support: https://lispcookbook.github.io/cl-cookbook/editor-support.ht... Emacs is first class, Portacle is an Emacs easy to install (3 clicks), Vim, Atom support is (was?) very good, Sublime Text seems good (it has an interactive debugger with stack frame inspection), VSCode sees good work underway, the Alive extension is new, usable but hard to install yet, LispWorks is proprietary and is more like Smalltalk, with many graphical windows to inspect your running application, Geany has simple and experimental support, Eclipse has basic support, Lem is a general purpose editor written in CL, it is Emacs-like and poorely documented :( we have Jupyter notebooks and simpler terminal-based interactive REPLs: cl-repl is like ipython.
So, one could complain five years ago easily about the lack of editor support, know your complaint should be more evolved than a Emacs/Vim dichotomy.
- package managers: Quicklisp is great, very slick and the ecosystem is very stable. When/if you encounter its limitations, you can use: Ultralisp, a Quicklisp distribution that ships every 5 minutes (but it doesn't check that all packages load correctly together), Qlot is used for project-local dependencies, where you pin each one precisely, CLPM is a new package manager that fixes some (all?) Quicklisp limitations
> [unicode, threading, GC…] All of these features are left to be implemented by third-party libraries
this leads to think that no implementation implements unicode or threading support O_o
> most of the language proper is not generic
mention generic-cl? https://github.com/alex-gutev/generic-cl/ (tried quickly, not intensively)
Documentation: fair points, but improving etc. Example of a new doc generator: https://40ants.com/doc/
Also I'd welcome a discussion about Coalton (Haskell-like type system on top of CL).
-
Modern sequence abstractions
Does generic-cl work for you? In recent months, u/alex-gutev worked on it to separate it out into smaller subsystems.
-
Common Lisp polymorphic stories.
Compared to generic-cls equality generic here: https://github.com/alex-gutev/generic-cl/blob/master/src/comparison/equality.lisp
-
alex-gutev/cl-form-types - Library for determining the types of Common Lisp forms based on information stored in the environment.
Thanks for sharing. I'm planning to use it in static-dispatch to further optimize generic function calls in generic-cl. It's also used in the lisp-polymorph project, work in progress not yet completed, which aims to provide an extensible generic interface, though not based on generic functions, to functions in the Common Lisp standard, like generic-cl however built from the ground up with performance and optimization and performance in mind.
-
Static-Dispatch 0.5: Improved inlining on SBCL and performance improvements for generic-cl
Release 0.5 adds a specialized implementation for SBCL which leverages the compiler's type inference engine, using DEFTRANSFORM, to allow for generic function inlining in a much broader range of scenarios. Any generic function call for which the types of the arguments can be determined by SBCL, can now be inlined by static-dispatch. This means even generic function calls with arguments consisting of complex expressions can be inlined. This also provides a performance boost for generic-cl where theoretically on SBCL, generic-cl:= should be equivalent in performance to cl:= in most cases where the type of the argument can be vaguely inferred by SBCL.
-
State of the Common Lisp ecosystem, 2020
If you want to regain performance, add-in type declarations, cl-generic will inline its functions: https://github.com/alex-gutev/generic-cl/#optimization
-
Re-export renamed symbols from other packages.
This example makes me think of generic-cl: https://github.com/alex-gutev/generic-cl/#add-nary
DifferentialEquations.jl
-
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/
-
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,
-
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.
-
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.
-
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?
coalton - Coalton is an efficient, statically typed functional programming language that supercharges Common Lisp.
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
static-dispatch - Static generic function dispatch for Common Lisp
diffeqpy - Solving differential equations in Python using DifferentialEquations.jl and the SciML Scientific Machine Learning organization
reagent - A minimalistic ClojureScript interface to React.js
Gridap.jl - Grid-based approximation of partial differential equations in Julia
inlined-generic-function - Bringing the speed of Static Dispatch to CLOS. Succeeded by https://github.com/marcoheisig/fast-generic-functions
ApproxFun.jl - Julia package for function approximation
drracket - DrRacket, IDE for Racket
DiffEqBase.jl - The lightweight Base library for shared types and functionality for defining differential equation and scientific machine learning (SciML) problems
lisp-interface-library - LIL: abstract interfaces and supporting concrete data-structures in Common Lisp
FFTW.jl - Julia bindings to the FFTW library for fast Fourier transforms