Julia bindings for the Enzyme automatic differentiator

Update immutable data

Primitives for construction of objects

AD-backend agnostic system defining custom forward and reverse mode rules. This is the light weight core to allow you to define rules for your functions in your packages, without depending on any particular AD system.

Forward Mode Automatic Differentiation for Julia

SciMLOperators.jl: Matrix-Free Operators for the SciML Scientific Machine Learning Common Interface in Julia

Julia implementation of Data structures

Julia macro for re-exporting one module from another

Julia wrapper for the LLVM C API

Formatted output of timed sections in Julia

An optimized graphs package for the Julia programming language

Heterogeneous programming in Julia

Repository for automatic differentiation backend types

Reusable compiler infrastructure for Julia GPU backends.

Reduce time-to-first-execution of Julia code

An optimized graphs package for the Julia programming language

Compatibility across Julia versions

Extensions for Julia's docsystem.

Fast jacobian computation through sparsity exploitation and matrix coloring

Designs for new Base array interface primitives, used widely through scientific machine learning (SciML) and other organizations

A Julia Basket of Hand-Picked Krylov Methods

Julia implementation of zlib decompression

Statically sized arrays for Julia

Update deeply nested immutable structs.

Interface designs for enforcing static computations in array functions with Julia

MacroTools provides a library of tools for working with Julia code and expressions.

Julia implementation of associative containers that preserve insertion order

Julia interface to Metis graph partitioning

Interface for function inversion in Julia

The Arnoldi Method with Krylov-Schur restart, natively in Julia.

SparseArrays.jl is a Julia stdlib

Fast non-allocating calculations of gradients, Jacobians, and Hessians with sparsity support

Naïve combined subexpression elimination in Julia

Interface for transformation functions in Julia

A simple shared suite of common derivative definitions

Special mathematical functions in Julia

Static types useful for dispatch and generated functions.

Create and control multiple Julia processes remotely for distributed computing. Ships as a Julia stdlib.

Simple Traits for Julia

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