Speaker
Description
The compiler research group pioneered interactive C++ notebooks with xeus-clang-repl, and its successor xeus-cpp. The ability to write automatic differentiation code in an interactive context eliminates the need for long edit-compile-run cycles and simplifies the approach to teaching computational methods.
By leveraging the clang-repl C/C++ interpreter, we create an interactive notebook environment for teaching autodiff concepts and evaluating the efficiency and correctness of differentiated code. This approach combines the performance of compiled C++ with the accessibility of Jupyter notebooks, making advanced automatic differentiation techniques more approachable for students and researchers.
This talk demonstrates how various C++ automatic differentiation tools, such as CoDiPack, Clad and boost-autodiff integrate with the xeus-cpp Jupyter kernel to enable interactive differentiable programming.
