Speaker
Valentin Churavy
(Johannes-Gutenberg Universität Mainz & Universität Augsburg)
Description
Automatic Differentiation (AD) is an important technique for both scientific computing and machine learning. AD frameworks from the machine learning world often lack the ability to differentiate programming patterns common in scientific computing, such as mutation and parallelism.
In my talk, I will cover the AD framework in Enzyme and how it can be used to differentiate scientific codes in Julia. While my talk will focus on Julia, Enzyme is LLVM-based and can also be used to differentiate C/C++/Fortran.
I will show how one can use Enzyme to differentiate scientific codes in Julia, how to extract Jacobians through directional derivatives and use it to formulate matrix-free methods.
| I want to participate in the youngRSE prize | yes |
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Primary author
Valentin Churavy
(Johannes-Gutenberg Universität Mainz & Universität Augsburg)
