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Istituto di Scienza e Tecnologie dell'Informazione     
Lucchese C., Nardini F. M., Orlando S., Perego R., Tonellotto N., Venturini R. Exploiting CPU SIMD extensions to speed-up document scoring with tree ensembles. In: SIGIR'16 - 39th International ACM SIGIR conference on Research and Development in Information Retrieval (Pisa, Italy, 17-21 July 2016). Proceedings, pp. 833 - 836. ACM, 2016.
 
 
Abstract
(English)
Scoring documents with learning-to-rank (LtR) models based on large ensembles of regression trees is currently deemed one of the best solutions to effectively rank query results to be returned by large scale Information Retrieval systems. This paper investigates the opportunities given by SIMD capabilities of modern CPUs to the end of efficiently evaluating regression trees ensembles. We propose V-QuickScorer (vQS), which exploits SIMD extensions to vectorize the document scoring, i.e., to perform the ensemble traversal by evaluating multiple documents simultaneously. We provide a comprehensive evaluation of vQS against the state of the art on three publicly available datasets. Experiments show that vQS provides speed-ups up to a factor of 3.2x.
URL: http://dl.acm.org/citation.cfm?doid=2911451.2914758
DOI: 10.1145/2911451.2914758
Subject Learning to rank
Efficient scoring
H.3.3 INFORMATION STORAGE AND RETRIEVAL. Information Search and Retrieval


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