Journal
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
Volume 26, Issue 9, Pages 2281-2292Publisher
IEEE COMPUTER SOC
DOI: 10.1109/TKDE.2013.73
Keywords
Web search; general information storage and retrieval; information technology and systems; scalability and efficiency; learning to rank; regression trees
Categories
Funding
- U.S. NSF [IIS-0916043, IIS-1144034, IIS-1218043]
- Dutch National Program COMMIT
- Direct For Computer & Info Scie & Enginr
- Div Of Information & Intelligent Systems [1144034] Funding Source: National Science Foundation
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Tree-based models have proven to be an effective solution for web ranking as well as other machine learning problems in diverse domains. This paper focuses on optimizing the runtime performance of applying such models to make predictions, specifically using gradient-boosted regression trees for learning to rank. Although exceedingly simple conceptually, most implementations of tree-based models do not efficiently utilize modern superscalar processors. By laying out data structures in memory in a more cache-conscious fashion, removing branches from the execution flow using a technique called predication, and micro-batching predictions using a technique called vectorization, we are able to better exploit modern processor architectures. Experiments on synthetic data and on three standard learning-to-rank datasets show that our approach is significantly faster than standard implementations.
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