NDLI: Evaluation of Likelihood Functions for Data Analysis on Graphics Processing Units

Content Provider	IEEE Xplore Digital Library
Author	Jarp, S. Lazzaro, A. Leduc, J. Nowak, A. Pantaleo, F.
Copyright Year	2011
Abstract	Data analysis techniques based on likelihood function calculation play a crucial role in many High Energy Physics measurements. Depending on the complexity of the models used in the analyses, with several free parameters, many independent variables, large data samples, and complex functions, the calculation of the likelihood functions can require a long CPU execution time. In the past, the continuous gain in performance for each single CPU core kept pace with the increase on the complexity of the analyses, maintaining reasonable the execution time of the sequential software applications. Nowadays, the performance for single cores is not increasing as in the past, while the complexity of the analyses has grown significantly in the Large Hadron Collider era. In this context a breakthrough is represented by the increase of the number of computational cores per computational node. This allows to speed up the execution of the applications, redesigning them with parallelization paradigms. The likelihood function evaluation can be parallelized using data and task parallelism, which are suitable for CPUs and GPUs (Graphics Processing Units), respectively. In this paper we show how the likelihood function evaluation has been parallelized on GPUs. We describe the implemented algorithm and we give some performance results when running typical models used in High Energy Physics measurements. In our implementation we achieve a good scaling with respect to the number of events of the data samples.
Starting Page	1349
Ending Page	1358
File Size	1015255
Page Count	10
File Format	PDF
ISBN	9781612844251
ISSN	15302075
DOI	10.1109/IPDPS.2011.296
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-05-16
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis Estimation Complexity theory Arrays Physics Graphics processing unit
Content Type	Text
Resource Type	Article

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