PUMA
Istituto di Scienza e Tecnologie dell'Informazione     
Lucchese C., Nardini F. M., Orlando S., Perego R., Tonellotto N., Venturini R. Speeding-up document scoring with tree ensembles using CPU SIMD extensions. In: IIR 2016 - 7th Italian Information Retrieval Workshop (Venezia, Italy, 30-31 May 2016). Abstract, article n. 7. (CEUR Workshop Proceedings). CEUR-WS, 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 extended abstract shortly summa- rizes the work in [4] proposing V-QuickScorer (vQS), an algorithm which exploits SIMD vector extensions on modern CPUs to perform the traversal of the ensamble in parallel by evaluating multiple documents simultaneously. We summarize the results of a comprehensive evaluation of vQS against state-of-the-art scoring algorithms showing that vQS outperforms competitors with speed-ups up to a factor of 2.4x.
URL: http://ceur-ws.org/Vol-1653/paper_7.pdf
Subject Learning to rank
Efficiency
H.3.3 INFORMATION STORAGE AND RETRIEVAL. Information Search and Retrieval


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