Garima Singh

Research Intern at CERN

email: garimasingh0028@gmail.com

Education: B. Tech in Information Technology, Manipal Institute of Technology, Manipal, India

Complete project: Add Automatic Differentiation to RooFit
RooFit is a toolkit for statistical modeling and fitting used by most experiments in particle physics. Just as data sets from next-generation experiments grow, processing requirements for physics analysis become more computationally demanding, necessitating performance optimizations for RooFit. One possibility to speed-up minimization and add stability is the use of automatic differentiation (AD). Unlike for numerical differentiation, the computation cost scales linearly with the number of parameters, making AD particularly appealing for statistical models with many parameters. The goal of this project is to add preliminary support for AD in RooFit and develop benchmarks to demonstrate the advantages of AD for large statistical fits using RooFit.

Project Proposal: URL

Mentors: Vassil Vassilev, David Lange

Completed project: Add Numerical Differentiation Support in Clad
In mathematics and computer algebra, automatic differentiation (AD) is a set of techniques to numerically evaluate the derivative of a function specified by a computer program. Automatic differentiation is an alternative technique to Symbolic differentiation and Numerical differentiation (the method of finite differences). Clad is based on Clang which provides the necessary facilities for code transformation. The AD library can differentiate non-trivial functions, find a partial derivative for trivial cases, and has good unit test coverage. In several cases, due to different limitations, it is either inefficient or impossible to differentiate a function. For example, clad cannot differentiate declared-but-not-defined functions. In that case, it issues an error. Instead, clad should fall back to its future numerical differentiation facilities.

Project Proposal: URL

Project Reports: Final Report

Mentors: Vassil Vassilev, Alexander Penev

Completed project: Floating point error evaluation with Clad
Floating-point estimation errors have been a testament to the finite nature of computing. Moreover, the predominance of Floating-point numbers in real-valued computation does not help that fact. Float computations are highly dependent on precision, and in most cases, very high precision calculation is not only not possible but very inefficient. Here, one has no choice but to resort to lower precision computing, which in turn is quite prone to errors. These errors result in inaccurate and sometimes catastrophic results; hence, it is imperative to estimate these errors accurately. This project aims to use Clad, a source transformation AD tool for C++ implemented as a plugin for the C++ compiler Clang, to develop a generic error estimation framework that is not bound to a particular error approximation model. It will allow users to select their preferable estimation logic and automatically generate functions augmented with code for the specified error estimator.

Project Proposal: URL

Project Reports: GSoC 2020 Archive

Mentors: Vassil Vassilev, David Lange

Presentations

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