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GPU-based Parallelization of QuickScorer to Speed-up Document Ranking with Tree Ensembles

Francesco Lettich, Claudio Lucchese, Franco Maria Nardini, Salvatore Orlando, Raffaele Perego, Nicola Tonellotto, Rossano Venturini
2016 Italian Information Retrieval Workshop  
To this end we propose GPUSCORER, a GPU-based parallelization of the state-of-the-art algorithm QUICKSCORER to score documents with tree ensembles.  ...  Scoring documents with learning-to-rank (LtR) models based on large ensembles of regression trees currently represents one of the most effective solutions to rank query results returned by large scale  ...  In this work we propose GPUSCORER, a GPU-based parallelization of QUICKSCO-RER [1] , the state-of-the-art algorithm to score documents with tree ensembles.  ... 
dblp:conf/iir/LettichLNOPTV16 fatcat:l4z4bkc6cnbnhm46qq6sjfvcri

Parallel Traversal of Large Ensembles of Decision Trees

Claudio Lucchese, Franco Maria Nardini, Salvatore Orlando, Raffaele Perego, Nicola Tonellotto, Rossano Venturini, Francesco Lettich
2018 Zenodo  
Our best results are obtained by the GPU-based parallelization of the state-of-the-art algorithm, with speedups of up to 102.6x.  ...  This paper investigates multi/many-core parallelization strategies for speeding up the traversal of large ensembles of regression trees thus obtaining machine-learnt models that are, at the same time,  ...  /many-core parallelism to speed-up both their training and testing [6] , [7] .  ... 
doi:10.5281/zenodo.2668379 fatcat:rtp6am3q2femfeweu7tkuknf64

Fast Ranking with Additive Ensembles of Oblivious and Non-Oblivious Regression Trees

Domenico Dato, Claudio Lucchese, Franco Maria Nardini, Salvatore Orlando, Raffaele Perego, Nicola Tonellotto, Rossano Venturini
2016 ACM Transactions on Information Systems  
Learning-to-Rank models based on additive ensembles of regression trees have been proven to be very effective for scoring query results returned by large-scale Web search engines.  ...  Unfortunately, the computational cost of scoring thousands of candidate documents by traversing large ensembles of trees is high.  ...  Within this research line, we recently proposed QUICKSCORER (QS), a new algorithm to score documents with ensembles of regression trees .  ... 
doi:10.1145/2987380 fatcat:ku3cfzwjhfbebnsexnh7xyjy74

QuickScorer

Claudio Lucchese, Franco Maria Nardini, Salvatore Orlando, Raffaele Perego, Nicola Tonellotto, Rossano Venturini
2015 Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval - SIGIR '15  
Learning-to-Rank models based on additive ensembles of regression trees have proven to be very effective for ranking query results returned by Web search engines, a scenario where quality and efficiency  ...  In this paper, we present QuickScorer, a new algorithm that adopts a novel bitvector representation of the tree-based ranking model, and performs an interleaved traversal of the ensemble by means of simple  ...  Acknowledgements We acknowledge the support of Tiscali S.p.A.  ... 
doi:10.1145/2766462.2767733 dblp:conf/sigir/LuccheseNOPTV15 fatcat:otj4v7qe2bfsdheu5ptunsbfle

Efficient Query Processing for Scalable Web Search

Nicola Tonellotto, Craig Macdonald, Iadh Ounis
2018 Foundations and Trends in Information Retrieval  
Our goal is to provide an accurate description of the basic search components involved in the scoring of documents in response to a query, together with a detailed and exhaustive review of the research  ...  In satisfying the information needs of millions of users, the effectiveness (the quality of the search results) and the efficiency (the speed at which the results are returned to the users) of a search  ...  Acknowledgements We would like to thank Maarten de Rijke for his patience and encouragements during the preparation of this manuscript, as well as the three anonymous reviewers for their constructive suggestions  ... 
doi:10.1561/1500000057 fatcat:wx53qhvfhnfwfc4hgdva5ypw3u

Distilled Neural Networks for Efficient Learning to Rank

Franco Maria Nardini, Cosimo Rulli, Salvatore Trani, Rossano Venturini
2022 IEEE Transactions on Knowledge and Data Engineering  
compared with tree-based ensembles, providing up to 4x scoring time speed-up without affecting the ranking quality.  ...  This result leads neural networks to become a natural competitor of tree-based ensembles on the ranking task.  ...  [35] propose a GPU version of QuickScorer, to exploit the massive parallelism of this computational engine.  ... 
doi:10.1109/tkde.2022.3152585 fatcat:kyvg4iir6fazfgneoveeqexgqa

LIPIcs : an Open-Access Series for International Conference Proceedings

Marc Herbstritt, Wolfgang Thomas
2016 ERCIM News  
Special theme: Machine Learning Machine-learnt models based on additive ensembles of regression trees have been shown to be very effective in several classification, regression, and ranking tasks.  ...  QS adopts a novel bit-vector representation of the tree-based model, and performs the traversal of the ensemble by means of simple logical bitwise operations.  ...  model of the application, i.e. the specification of the multi-cloud application requirements with respect to component interfaces, cloud deployment needs, etc.  ... 
doi:10.18154/rwth-2018-223393 fatcat:ddo7qz65l5b7peuksw2amaoxai

Dagstuhl Reports, Volume 6, Issue 10, September 2016, Complete Issue [article]

2017
topic of the seminar as well as to the list of participants.  ...  in the union of n arbitrary theories (including for instance propositional logic), as long as each of them comes with an MCSat-friendly inference system.  ...  Streaming Pattern Matching Quickscorer: a fast algorithm to rank documents with additive ensembles of regression trees 4 Working groups LZ78 Construction in Little Main Memory Space Diego Arroyuelo  ... 
doi:10.4230/dagrep.6.10 fatcat:wq33g6exi5bzll67no5ztodtoy