short-paper

Exploiting CPU SIMD Extensions to Speed-up Document Scoring with Tree Ensembles

Authors:

Claudio Lucchese,

Franco Maria Nardini,

Salvatore Orlando,

Raffaele Perego,

Nicola Tonellotto,

Rossano VenturiniAuthors Info & Claims

SIGIR '16: Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval

Pages 833 - 836

https://doi.org/10.1145/2911451.2914758

Published: 07 July 2016 Publication History

Get Access

Abstract

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.

References

[1]

N. Asadi, J. Lin, and A. P. de Vries. Runtime optimizations for tree-based machine learning models. IEEE Transactions on Knowledge and Data Engineering, 26(9):2281--2292, 2014.

Crossref

Google Scholar

[2]

G. Capannini, D. Dato, C. Lucchese, M. Mori, F. M. Nardini, S. Orlando, R. Perego, and N. Tonellotto. Quality versus Efficiency in Document Scoring with Learning-to-Rank Models. Information Processing and Management, 2016.

Google Scholar

[3]

J. H. Friedman. Greedy function approximation: a gradient boosting machine. Annals of Statistics, pages 1189--1232, 2001.

Crossref

Google Scholar

[4]

C. Lucchese, F. M. Nardini, S. Orlando, R. Perego, N. Tonellotto, and R. Venturini. Quickscorer: A fast algorithm to rank documents with additive ensembles of regression trees. In Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), pages 73--82. ACM, 2015.

Digital Library

Google Scholar

[5]

O. Polychroniou, A. Raghavan, and K. A. Ross. Rethinking simd vectorization for in-memory databases. In Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data, SIGMOD '15, pages 1493--1508, New York, NY, USA, 2015. ACM.

Digital Library

Google Scholar

[6]

Q. Wu, C. J. Burges, K. M. Svore, and J. Gao. Adapting boosting for information retrieval measures. Information Retrieval, 2010.

Digital Library

Google Scholar

Cited By

View all

Fröbe MMackenzie JMitra BNardini FPotthast MHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)ReNeuIR at SIGIR 2024: The Third Workshop on Reaching Efficiency in Neural Information RetrievalProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657994(3051-3054)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657994
Bruch SLucchese CNardini F(2023)Report on the 1st Workshop on Reaching Efficiency in Neural Information Retrieval (ReNeuIR 2022) at SIGIR 2022ACM SIGIR Forum10.1145/3582900.358291656:2(1-14)Online publication date: 31-Jan-2023
https://dl.acm.org/doi/10.1145/3582900.3582916
Bruch SMackenzie JMaistro MNardini FChen HDuh WHuang HKato MMothe JPoblete B(2023)ReNeuIR at SIGIR 2023: The Second Workshop on Reaching Efficiency in Neural Information RetrievalProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3591922(3456-3459)Online publication date: 19-Jul-2023
https://dl.acm.org/doi/10.1145/3539618.3591922
Show More Cited By

Index Terms

Exploiting CPU SIMD Extensions to Speed-up Document Scoring with Tree Ensembles
1. Computing methodologies
  1. Machine learning
    1. Machine learning algorithms
      1. Ensemble methods
2. Information systems
  1. Information retrieval
    1. Retrieval models and ranking
      1. Learning to rank

Recommendations

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

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 ...
Efficient execution of graph algorithms on CPU with SIMD extensions
CGO '21: Proceedings of the 2021 IEEE/ACM International Symposium on Code Generation and Optimization

Existing state-of-the-art CPU graph frameworks take advantage of multiple cores, but not the SIMD capability within each core. In this work, we retarget an existing GPU graph algorithm compiler to obtain the first graph framework that uses SIMD ...
Efficient aerial image simulation on multi-core SIMD CPU
ICCAD '13: Proceedings of the International Conference on Computer-Aided Design

Aerial image simulation is a fundamental problem in advanced lithography for chip fabrication. Since it requires a huge number of mathematical computations, an efficient yet accurate implementation becomes a necessity. In the literature, GPU or FPGA has ...

Comments

Information & Contributors

Information

Published In

SIGIR '16: Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval

July 2016

1296 pages

ISBN:9781450340694

DOI:10.1145/2911451

General Chairs:
Raffaele Perego
ISTI-CNR, Italy
,
Fabrizio Sebastiani
Qatar Computing Research Institute, HBKU, Qatar
,
Program Chairs:
Javed Aslam
Northeastern University, US
,
Ian Ruthven
University of Strathclyde, UK
,
Justin Zobel
University of Melbourne, Australia

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 July 2016

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Short-paper

Funding Sources

European Commission

Conference

SIGIR '16

Sponsor:

SIGIR

SIGIR '16: The 39th International ACM SIGIR conference on research and development in Information Retrieval

July 17 - 21, 2016

Pisa, Italy

Acceptance Rates

SIGIR '16 Paper Acceptance Rate 62 of 341 submissions, 18%;

Overall Acceptance Rate 792 of 3,983 submissions, 20%

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

22
Total Citations
View Citations
268
Total Downloads

Downloads (Last 12 months)13
Downloads (Last 6 weeks)0

Reflects downloads up to 27 Jul 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Fröbe MMackenzie JMitra BNardini FPotthast MHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)ReNeuIR at SIGIR 2024: The Third Workshop on Reaching Efficiency in Neural Information RetrievalProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657994(3051-3054)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657994
Bruch SLucchese CNardini F(2023)Report on the 1st Workshop on Reaching Efficiency in Neural Information Retrieval (ReNeuIR 2022) at SIGIR 2022ACM SIGIR Forum10.1145/3582900.358291656:2(1-14)Online publication date: 31-Jan-2023
https://dl.acm.org/doi/10.1145/3582900.3582916
Bruch SMackenzie JMaistro MNardini FChen HDuh WHuang HKato MMothe JPoblete B(2023)ReNeuIR at SIGIR 2023: The Second Workshop on Reaching Efficiency in Neural Information RetrievalProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3591922(3456-3459)Online publication date: 19-Jul-2023
https://dl.acm.org/doi/10.1145/3539618.3591922
Nardini FRulli CTrani SVenturini R(2023)Distilled Neural Networks for Efficient Learning to RankIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2022.315258535:5(4695-4712)Online publication date: 1-May-2023
https://dl.acm.org/doi/10.1109/TKDE.2022.3152585
Van Lunteren J(2023)Accelerating Decision-Tree-Based Inference Through Adaptive Parallelization2023 32nd International Conference on Parallel Architectures and Compilation Techniques (PACT)10.1109/PACT58117.2023.00023(176-186)Online publication date: 21-Oct-2023
https://doi.org/10.1109/PACT58117.2023.00023
Chen KSu CHakert CBuschjäger SLee CLee JMorik KChen J(2022)Efficient Realization of Decision Trees for Real-Time InferenceACM Transactions on Embedded Computing Systems10.1145/350801921:6(1-26)Online publication date: 18-Oct-2022
https://dl.acm.org/doi/10.1145/3508019
Dato DMacAvaney SNardini FPerego RTonellotto NAmigo ECastells PGonzalo JCarterette BCulpepper JKazai G(2022)The Istella22 DatasetProceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3477495.3531740(3099-3107)Online publication date: 6-Jul-2022
https://dl.acm.org/doi/10.1145/3477495.3531740
Bruch SLucchese CNardini FAmigo ECastells PGonzalo JCarterette BCulpepper JKazai G(2022)ReNeuIR: Reaching Efficiency in Neural Information RetrievalProceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3477495.3531704(3462-3465)Online publication date: 6-Jul-2022
https://dl.acm.org/doi/10.1145/3477495.3531704
Prasad ARajendra SRajan KGovindarajan RBondhugula UHardavellas NCampanoni SGrot BKarpuzcu U(2022)Treebeard: An Optimizing Compiler for Decision Tree Based ML InferenceProceedings of the 55th Annual IEEE/ACM International Symposium on Microarchitecture10.1109/MICRO56248.2022.00043(494-511)Online publication date: 1-Oct-2022
https://dl.acm.org/doi/10.1109/MICRO56248.2022.00043
Hakert CChen KChen J(2022)Immediate Split Trees: Immediate Encoding of Floating Point Split Values in Random ForestsMachine Learning and Knowledge Discovery in Databases10.1007/978-3-031-26419-1_32(531-546)Online publication date: 19-Sep-2022
https://dl.acm.org/doi/10.1007/978-3-031-26419-1_32
Show More Cited By

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Cited By

Index Terms

Recommendations

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

Efficient execution of graph algorithms on CPU with SIMD extensions

Efficient aerial image simulation on multi-core SIMD CPU