New RooFit Developments to Speed up your Analysis

As the field of high energy physics moves to an era of precision measurements its models become ever more complex and so do the challenges for computational frameworks that intend to fit these models to data. This report describes two computational optimizations with which RooFit intends to address this challenge: parallelization and batched computations. For the former, a problem-agnostic parallelization framework was devised with generality in mind such that it could be seamlessly applied at various stages of the existing Minuit2 minimization routine. In the results shown in this report parallelization was applied at the gradient calculation stage. The batched computations approach on the other hand required an overhaul of the current manner in which RooFit prepares its computatational graph for the evaluation of likelihoods. This report includes initial benchmarks of the batched computations strategy run on a CPU with vector instructions. Both strategies show significant performance improvements and the parallelization approach at its current state also proves to be robust enough to consistently fit state of the art physics models to real LHC data. Future developments are targeted towards combining both technologies in RooFit in a production-ready state in a ROOT release in the near future.