Paper: A Multi-Teraflop Constituency Parser using GPUs

ACL ID D13-1195
Title A Multi-Teraflop Constituency Parser using GPUs
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2013

Constituency parsing with rich grammars re- mains a computational challenge. Graph- ics Processing Units (GPUs) have previously been used to accelerate CKY chart evalua- tion, but gains over CPU parsers were mod- est. 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 eval- uations 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 sym- bol blocking, and cache-sharing.