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
BibTeX

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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