Composing Finite State Transducers on GPUs

Arturo Argueta, David Chiang


Abstract
Weighted finite state transducers (FSTs) are frequently used in language processing to handle tasks such as part-of-speech tagging and speech recognition. There has been previous work using multiple CPU cores to accelerate finite state algorithms, but limited attention has been given to parallel graphics processing unit (GPU) implementations. In this paper, we introduce the first (to our knowledge) GPU implementation of the FST composition operation, and we also discuss the optimizations used to achieve the best performance on this architecture. We show that our approach obtains speedups of up to 6 times over our serial implementation and 4.5 times over OpenFST.
Anthology ID:
P18-1251
Volume:
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
July
Year:
2018
Address:
Melbourne, Australia
Editors:
Iryna Gurevych, Yusuke Miyao
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2697–2705
Language:
URL:
https://aclanthology.org/P18-1251
DOI:
10.18653/v1/P18-1251
Bibkey:
Cite (ACL):
Arturo Argueta and David Chiang. 2018. Composing Finite State Transducers on GPUs. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2697–2705, Melbourne, Australia. Association for Computational Linguistics.
Cite (Informal):
Composing Finite State Transducers on GPUs (Argueta & Chiang, ACL 2018)
Copy Citation:
PDF:
https://aclanthology.org/P18-1251.pdf
Poster:
 P18-1251.Poster.pdf