Media Summary: Have you ever wondered how many FLOPS your CPU or GPU actually performs when executing (parts of) your This talk was presented as part of JuliaCon2021 Abstract: Enzyme ( is a reverse mode auto-differentiation ... In this tutorial, we explore the package LinearOperators.

Reactant Jl Optimize Julia Functions - Detailed Analysis & Overview

Have you ever wondered how many FLOPS your CPU or GPU actually performs when executing (parts of) your This talk was presented as part of JuliaCon2021 Abstract: Enzyme ( is a reverse mode auto-differentiation ... In this tutorial, we explore the package LinearOperators. In this lesson, you start learning how to write your own Enzyme is a new LLVM-based differentiation framework capable of creating fast derivatives in a variety of languages. In this talk ...

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Reactant: Optimize Julia functions with MLIR & XLA | Moses | JuliaCon Global 2025
Reactant.jl - Optimize Julia Functions for High Performance on CPU, GPU, TPU | Berg | Paris 2025
Compilers, MLIR, and Hardware Acceleration: The Origin Story of Reactant.jl
Using Optimization.jl to Seek the Optimal Optimiser in SciML | Vaibhav Dixit | JuliaCon 2022
Accelerating Machine Learning in Julia using Lux & Reactant | Pal | JuliaCon Global 2025
Imitation Learning | Decision Making Under Uncertainty using POMDPs.jl
Monitoring Performance on a Hardware Level With LIKWID.jl | Carsten Bauer | JuliaCon 2022
ImplicitDifferentiation.jl: Differentiating Implicit Functions | M. Tarek, G. Dalle | JuliaCon 2022
Enzyme.jl: Reverse mode diff'n on LLVM IR for Julia | V. Churavy, W. Moses | JuliaCon 2021
InferOpt.jl: combinatorial optimization and machine learning | Baty | JuliaCon 2024
Julia tutorial on LinearOperators.jl from JuliaSmoothOptimizers
User-Defined Functions in Julia | Julia Programming For Nervous Beginners (Week 2 Lesson 3)
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Reactant: Optimize Julia functions with MLIR & XLA | Moses | JuliaCon Global 2025

Reactant: Optimize Julia functions with MLIR & XLA | Moses | JuliaCon Global 2025

Reactant

Reactant.jl - Optimize Julia Functions for High Performance on CPU, GPU, TPU | Berg | Paris 2025

Reactant.jl - Optimize Julia Functions for High Performance on CPU, GPU, TPU | Berg | Paris 2025

"

Compilers, MLIR, and Hardware Acceleration: The Origin Story of Reactant.jl

Compilers, MLIR, and Hardware Acceleration: The Origin Story of Reactant.jl

In this episode of the

Using Optimization.jl to Seek the Optimal Optimiser in SciML | Vaibhav Dixit | JuliaCon 2022

Using Optimization.jl to Seek the Optimal Optimiser in SciML | Vaibhav Dixit | JuliaCon 2022

Optimization

Accelerating Machine Learning in Julia using Lux & Reactant | Pal | JuliaCon Global 2025

Accelerating Machine Learning in Julia using Lux & Reactant | Pal | JuliaCon Global 2025

Accelerating Machine Learning in

Imitation Learning | Decision Making Under Uncertainty using POMDPs.jl

Imitation Learning | Decision Making Under Uncertainty using POMDPs.jl

Github: https://github.com/JuliaAcademy/Decision-Making-Under-Uncertainty

Monitoring Performance on a Hardware Level With LIKWID.jl | Carsten Bauer | JuliaCon 2022

Monitoring Performance on a Hardware Level With LIKWID.jl | Carsten Bauer | JuliaCon 2022

Have you ever wondered how many FLOPS your CPU or GPU actually performs when executing (parts of) your

ImplicitDifferentiation.jl: Differentiating Implicit Functions | M. Tarek, G. Dalle | JuliaCon 2022

ImplicitDifferentiation.jl: Differentiating Implicit Functions | M. Tarek, G. Dalle | JuliaCon 2022

We present a

Enzyme.jl: Reverse mode diff'n on LLVM IR for Julia | V. Churavy, W. Moses | JuliaCon 2021

Enzyme.jl: Reverse mode diff'n on LLVM IR for Julia | V. Churavy, W. Moses | JuliaCon 2021

This talk was presented as part of JuliaCon2021 Abstract: Enzyme (https://enzyme.mit.edu) is a reverse mode auto-differentiation ...

InferOpt.jl: combinatorial optimization and machine learning | Baty | JuliaCon 2024

InferOpt.jl: combinatorial optimization and machine learning | Baty | JuliaCon 2024

InferOpt.

Julia tutorial on LinearOperators.jl from JuliaSmoothOptimizers

Julia tutorial on LinearOperators.jl from JuliaSmoothOptimizers

In this tutorial, we explore the package LinearOperators.

User-Defined Functions in Julia | Julia Programming For Nervous Beginners (Week 2 Lesson 3)

User-Defined Functions in Julia | Julia Programming For Nervous Beginners (Week 2 Lesson 3)

In this lesson, you start learning how to write your own

Fast Forward and Reverse-Mode Differentiation via Enzyme.jl | Many speakers | JuliaCon 2022

Fast Forward and Reverse-Mode Differentiation via Enzyme.jl | Many speakers | JuliaCon 2022

Enzyme is a new LLVM-based differentiation framework capable of creating fast derivatives in a variety of languages. In this talk ...