Palombo, G. (2012) Comparison of the CPU and memory performance of StatPatternRecognitions (SPR) and Toolkit for MultiVariate Analysis (TMVA). Nuclear Instruments and Methods in Physics Research A, 672 . pp. 6-12. ISSN 0168-9002 http://resolver.caltech.edu/CaltechAUTHORS:20120411-111251068
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High Energy Physics data sets are often characterized by a huge number of events. Therefore, it is extremely important to use statistical packages able to efficiently analyze these unprecedented amounts of data. We compare the performance of the statistical packages StatPatternRecognition (SPR) and Toolkit for MultiVariate Analysis (TMVA). We focus on how CPU time and memory usage of the learning process scale versus data set size. As classifiers, we consider Random Forests, Boosted Decision Trees and Neural Networks only, each with specific settings. For our tests, we employ a data set widely used in the machine learning community, “Threenorm” data set, as well as data tailored for testing various edge cases. For each data set, we constantly increase its size and check CPU time and memory needed to build the classifiers implemented in SPR and TMVA. We show that SPR is often significantly faster and consumes significantly less memory. For example, the SPR implementation of Random Forest is by an order of magnitude faster and consumes an order of magnitude less memory than TMVA on Threenorm data.
|Additional Information:||© 2012 Published by Elsevier B.V. Received 19 June 2011. Revised 5 December 2011. Accepted 5 December 2011. Available online 21 December 2011. Thanks to Ilya Narsky and Julian Bunn for insightful suggestions and reviewing earlier drafts of this work.|
|Subject Keywords:||StatPatternRecognition; Toolkit for MultiVariate Analysis; Machine learning; Data analysis; Computational physics|
|Official Citation:||G. Palombo, Comparison of the CPU and memory performance of StatPatternRecognitions (SPR) and Toolkit for MultiVariate Analysis (TMVA), Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, Volume 672, 21 April 2012, Pages 6-12, ISSN 0168-9002, 10.1016/j.nima.2011.12.044.|
|Usage Policy:||No commercial reproduction, distribution, display or performance rights in this work are provided.|
|Deposited By:||Tony Diaz|
|Deposited On:||11 Apr 2012 20:17|
|Last Modified:||11 Apr 2012 20:17|
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