GooFit: A library for massively parallelising maximum-likelihood fits

Fitting complicated models to large datasets is a bottleneck of many analyses. We present GooFit, a library and tool for constructing arbitrarily-complex probability density functions (PDFs) to be evaluated on nVidia GPUs or on multicore CPUs using OpenMP. The massive parallelisation of dividing up...

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Bibliographic Details
Published inarXiv.org
Main Authors Andreassen, R, Meadows, B T, de Silva, M, Sokoloff, M D, Tomko, K
Format Paper Journal Article
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 07.11.2013
Subjects
Computer Science - Distributed, Parallel, and Cluster Computing
Computer Science - Mathematical Software
Mathematical analysis
Probability density functions
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Summary:Fitting complicated models to large datasets is a bottleneck of many analyses. We present GooFit, a library and tool for constructing arbitrarily-complex probability density functions (PDFs) to be evaluated on nVidia GPUs or on multicore CPUs using OpenMP. The massive parallelisation of dividing up event calculations between hundreds of processors can achieve speedups of factors 200-300 in real-world problems.
ISSN:2331-8422
DOI:10.48550/arxiv.1311.1753