DocumentCode
108255
Title
Implementation of a Thread-Parallel, GPU-Friendly Function Evaluation Library
Author
Andreassen, Rolf E. ; de Silva, Weeraddana Manjula ; Meadows, Brian T. ; Sokoloff, Michael D. ; Tomko, Karen A.
Author_Institution
Phys. Dept., Univ. of Cincinnati, Cincinnati, OH, USA
Volume
2
fYear
2014
fDate
2014
Firstpage
160
Lastpage
176
Abstract
GooFit is a thread-parallel, GPU-friendly function evaluation library, nominally designed for use with the maximum likelihood fitting program MINUIT. In this use case, it provides highly parallel calculations of normalization intergrals and log (likelihood) sums. A key feature of the design is its use of the Thrust library to manage all parallel kernel launches. This allows GooFit to execute on any architecture for which Thrust has a backend, currently, including CUDA for nVidia GPUs and OpenMP for single- and multicore CPUs. Running on an nVidia C2050, GooFit executes 300 times more quickly for a complex high energy physics problem than does the prior (algorithmically equivalent) code running on a single CPU core. The design and implementation choices, discussed in detail, can help to guide developers of other highly parallel, compute-intensive libraries.
Keywords
application program interfaces; graphics processing units; maximum likelihood estimation; parallel architectures; parallel programming; software libraries; CUDA; GooFit; MINUIT maximum likelihood fitting program; OpenMP; complex high energy physic problem; log sums; multicore CPUs; nVidia C2050; nVidia GPUs; normalization intergrals; parallel kernel management; parameter estimation; thread-parallel GPU-friendly function evaluation library; thrust library; Graphics processing units; Libraries; Maximum likelihood estimation; Parallel processing; Parallel programming; Parameter estimation; Parallel processing; parallel programming; parameter estimation; parameter extraction;
fLanguage
English
Journal_Title
Access, IEEE
Publisher
ieee
ISSN
2169-3536
Type
jour
DOI
10.1109/ACCESS.2014.2306895
Filename
6746000
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