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GooFit: A library for massively parallelising maximum-likelihood fits

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arxiv 1311.1753 v1 pith:RNPA766Y submitted 2013-11-07 cs.DC cs.MS

GooFit: A library for massively parallelising maximum-likelihood fits

R. Andreassen , B. T. Meadows , M. de Silva , M. D. Sokoloff , K. Tomko This is my paper
classification cs.DC cs.MS
keywords goofitlibraryachieveanalysesarbitrarily-complexbottleneckcalculationscomplicated
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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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.

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