Speaker
Description
RooFit is a library for building and fitting statistical models that is part of ROOT. It is used in most experiments in particle physics, in particular, the LHC experiments. Recently, the backend that evaluates the RooFit likelihood functions was rewritten to support performant computations of model components on different hardware. This new backend is referred to as the "batch mode". So far, it supports GPUs with CUDA and also the vectorizing instructions on the CPU. With ROOT 6.28, the new batch mode is feature-complete and speeds up all use cases targeted by RooFit, even on a single CPU thread. The GPU backend further reduces the likelihood evaluation time, particularly for unbinned fits to large datasets. The speedup is most significant when all likelihood components support GPU evaluation. Still, if this is not the case, the backend will optimally distribute the computation on the CPU and GPU to guarantee a speedup.
RooFit is a very extensible library with a vast user interface to inject behavior changes at almost every point of the likelihood calculation, which the new heterogeneous computation backend must handle. This presentation discusses our approach and lessons learned when facing this challenge. The highlight of this contribution is showcasing the performance improvements for benchmark examples, fits from the RooFit tutorials, and real-world fit examples from LHC experiments. We will also elaborate on how users can implement GPU support for their custom probability density functions and explain the current limitations and future developments.
| Consider for long presentation | No |
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