Open AccessA Fast and Scalable Manycore Implementation for an On-Demand Learning to Rank Method
Author(s) -
Mateus F. Freitas,
Daniel De Sousa,
Wellington Santos Martins,
Thierson Couto Rosa,
Rodrigo Silva,
Marcos André Gonçalves
Publication year - 2016
Language(s) - English
Resource type - Conference proceedings
DOI - 10.5753/wscad.2016.14254
Subject(s) - speedup , scalability , computer science , ranking (information retrieval) , baseline (sea) , learning to rank , rank (graph theory) , parallel computing , artificial intelligence , machine learning , database , oceanography , mathematics , combinatorics , geology
Learning to rank (L2R) works by constructing a ranking model from training data so that, given unseen data (query), a somewhat similar ranking is produced. Almost all work in L2R focuses on ranking accuracy leaving performance and scalability overlooked. In this work we present a fast and scalable manycore (GPU) implementation for an on-demand L2R technique that builds ranking models on the y. Our experiments show that we are able to process a query (build a model and rank) in only a few milliseconds, achieving a speedup of 508x over a serial baseline and 4x over a parallel baseline for the best case. We extend the implementation to work with multiple GPUs, further increasing the speedup over the parallel baseline to approximately x16 when using 4 GPUs.