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Thursday, June 22 • 4:21pm - 4:57pm
Mixed-Mode Automatic Differentiation in Julia

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Julia's unique execution model, metaprogramming facilities, and type system make it an ideal candidate language for native automatic differentiation (AD). In this talk, we'll discuss a variety of Julia-specific tricks employed by ForwardDiff and ReverseDiff to differentiate user-provided Julia functions. Topics covered include the implementation of a native Julia execution tracer via operator overloading, functor-based directives for specialized instruction taping, SIMD vectorization and instruction elision for inlined dual number operations, and vectorized differentiation of linear algebraic expressions. I'll close the talk with a glimpse into the future of AD in Julia and JuMP, highlighting the effect new features may have on other downstream projects like Celeste, Optim and RigidBodyDynamics.


Jarrett Revels

I like to make Julia code differentiate itself.

Thursday June 22, 2017 4:21pm - 4:57pm
West Pauley Pauley Ballroom, Berkeley, CA