When Enzyme meets JuMP: a tour de ronde

07/28/2023, 4:00 PM4:30 PM UTC
32-155

Abstract:

Julia provides a vibrant automatic differentiation (AD) ecosystem, with numerous AD libraries. All these AD solutions are unique, and take diverse approaches to the various fundamental AD design choices for code transformations available in Julia. The recent refactoring of the JuMP nonlinear interface is giving us an opportunity to integrate some of these AD libraries into JuMP. However, how far can we go in the integration?

Description:

In this talk, we present our recent work with Enzyme, an AD backend working directly at the LLVM level, enabling massively parallel or vectorized modeling through GPUCompiler.jl. We put a special emphasis on the extraction of sparse Jacobian and sparse Hessian with Enzyme using respectively forward and forward-over-reverse automatic differentiation. We give a thorough investigation of the capability of Enzyme regarding nonlinear programming and present detailed results on the seminal optimal power flow (OPF) problem.

Platinum sponsors

JuliaHub

Gold sponsors

ASML

Silver sponsors

Pumas AIQuEra Computing Inc.Relational AIJeffrey Sarnoff

Bronze sponsors

Jolin.ioBeacon BiosignalsMIT CSAILBoeing

Academic partners

NAWA

Local partners

Postmates

Fiscal Sponsor

NumFOCUS