*IRIS-HEP Fellow*

*email: petro.zarytskyi@gmail.com*

**Education:** Applied Mathematics, Taras Shevchenko National University of Kyiv, Ukraine, 2021-present

** Ongoing project:**
*Optimizing reverse-mode automatic differentiation with advanced activity-analysis*

Clad is an automatic differentiation clang plugin for C++. It automatically
generates code that computes derivatives of functions given by the user.
Clad can work in two main modes: forward and reverse. The reverse mode involves
computing the derivative by applying the chain rule to all the elementary operations
from the result to the argument. It turns out to be more
efficient when there are more dependent output variables than independent
input variables (e.g. calculating a gradient). However, the approach to blindly
compute the derivatives of all the intermediate variables obviously produces
code that does a lot of unnecessary calculations. With advanced activity analysis,
the variables which are not used to compute the result can be found and removed,
increasing the time- and memory- efficiency of the output code.

**Project Proposal:**
URL

**Mentors:** Vassil Vassilev, David Lange