A multi-Teraflop Constituency Parser using GPUs
UC Berkeley, Berkeley, CA, 94720
Conference on Empirical Methods in Natural Language Processing, 2013
@article{canny2013multi,
title={A multi-Teraflop Constituency Parser using GPUs},
author={Canny, John and Hall, David and Klein, Dan},
journal={Architecture},
volume={3},
pages={3–5},
year={2013}
}
Constituency parsing with rich grammars remains a computational challenge. Graphics Processing Units (GPUs) have previously been used to accelerate CKY chart evaluation, but gains over CPU parsers were modest. In this paper, we describe a collection of new techniques that enable chart evaluation at close to the GPU’s practical maximum speed (a Teraflop), or around a half-trillion rule evaluations per second. Net parser performance on a 4-GPU system is over 1 thousand length – 30 sentences/second (1 trillion rules/sec), and 400 general sentences/second for the Berkeley Parser Grammar. The techniques we introduce include grammar compilation, recursive symbol blocking, and cache-sharing.
October 24, 2013 by hgpu