10781

A multi-Teraflop Constituency Parser using GPUs

John Canny, David Hall, Dan Klein
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.
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