EnzymeAD/Enzyme.jl
Julia bindings for the Enzyme automatic differentiator
What it solves
Enzyme is designed to solve the problem of efficiently calculating gradients (automatic differentiation) for complex software. It allows developers to compute derivatives of functions written in high-performance languages, avoiding the need to manually rewrite code for machine learning or scientific computing tasks.
How it works
It operates as a plugin for LLVM, meaning it performs automatic differentiation on the compiler's intermediate representation of the code. By differentiating optimized code, it can achieve performance that meets or exceed state-of-the-art AD tools.
Who it’s for
This project is for developers and researchers in machine learning and high-performance computing (HPC) who need fast, efficient gradients for their existing codebases, particularly those utilizing Julia or LLVM-based languages.
Highlights
- own high-performance automatic differentiation
- LLVM plugin architecture for differentiating optimized code
- Support for reverse-mode automatic differentiation
- Optimized for GPU kernels and high-performance computing environments
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