JuMP.jl
MuladdMacro.jl
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| JuMP.jl | MuladdMacro.jl | |
|---|---|---|
| 3 | 3 | |
| 2,134 | 45 | |
| 1.5% | - | |
| 9.3 | 6.3 | |
| about 18 hours ago | 21 days ago | |
| Julia | Julia | |
| GNU General Public License v3.0 or later | GNU General Public License v3.0 or later |
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JuMP.jl
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Optimization
JuMP.jl is my personal go-to when solving "big" optimization problems in Julia (maybe it's overkill for your application).
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Multiple dispatch: Common Lisp vs Julia
A 100+ contributor project
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Julia macros
Macros are very useful if you want to create Domain Specific Languages (DSLs), see https://github.com/jump-dev/JuMP.jl or if you want to transpile a subset of Julia to another language or say GPU code.
MuladdMacro.jl
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Std: Clamp generates less efficient assembly than std:min(max,std:max(min,v))
Totally agreed. In Julia we use https://github.com/SciML/MuladdMacro.jl all over the place so that way it's contextual and does not bleed into other functions. fast-math changing everything is just... dangerous.
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Someone’s Been Messing with My Subnormals
But if what you want is automatic FMA, then why carry along every other possible behavior with it? Just because you want FMA, suddenly NaNs are turned into Infs, subnormal numbers go to zero, handling of sin(x) at small values is inaccurate, etc? To me that's painting numerical handling in way too broad of strokes. FMA also only increases numerical accuracy, it doesn't decrease numerical accuracy, so bundling it with unsafe transformations makes one uncertain now whether it has improved or decreased accuracy.
For reference, to handle this well we use MuladdMacro.jl which is a semantic transformation that turns x*y+z into muladd expressions, and it does not recurse into functions so it does not change the definitions of the callers inside of the macro scope.
https://github.com/SciML/MuladdMacro.jl
This is something that will always increase performance and accuracy (performance because muladd in Julia is an FMA that is only applied if hardware FMA exists, effectively never resorting to a software FMA emulation) because it's targeted to do only a transformation that has that property.
- Julia macros
What are some alternatives?
Catalyst.jl - Chemical reaction network and systems biology interface for scientific machine learning (SciML). High performance, GPU-parallelized, and O(1) solvers in open source software.
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
ParameterizedFunctions.jl - A simple domain-specific language (DSL) for defining differential equations for use in scientific machine learning (SciML) and other applications
ComponentArrays.jl - Arrays with arbitrarily nested named components.
SymbolicNumericIntegration.jl - SymbolicNumericIntegration.jl: Symbolic-Numerics for Solving Integrals
NumericalAlgorithms.jl - [DEPRECATED] Statistics & Numerical algorithms implemented in Julia.
Unityper.jl
OMLT - Represent trained machine learning models as Pyomo optimization formulations
ConstructiveGeometry.jl - Algorithms and syntax for building CSG objects within Julia.
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