IRIS-HEP Fellow
email: acherjan@mail.uc.edu
Education: Computer Sciences B.S. + M.S , University of Cincinnati, OH
Completed project:
Extend the Automatic Differentiation Support in RooFit
In terms of minimization time, Roofit offers faster results even with numerical
differentiation techniques as compared to minimizing a likelihood function that
is written by hand in C++, due its complex caching logic. Automatic differentiation
gives an additional speedup and more accuracy and scalability for problems with large
number of parameters. The purpose of this project will be to firstly use Minuit as
an optimization algorithm with externally provided gradients, extend support to cover
HistFactory and other parts of RooFit, and finally to optimize Clad generated derivatives a
nd further explore how they can be parallelized (OpenMP or CUDA).
Project Proposal: URL
Mentors: Vassil Vassilev, David Lange
We are part of the
Princeton Department of Physics
and
Princeton Institute for Computational Science and Engineering
(PICSciE).
Contact: David Lange; Vassil Vassilev
This project is supported by the National Science Foundation under Grant OAC-1931408 (2020-2023) and OAC-2311471(2023-2026). Any opinions, findings, conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.
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