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 6x over our serial implementation and 4.5x over OpenFST.

本论文介绍了第一篇知道的 GPU 实现权重有限状态转换器组合操作的方法，并讨论了用于在此体系结构上实现最佳性能的优化。我们展示了我们的方法可以比串行实现快 6 倍，并且比 OpenFST 快 4.5 倍。

在 GPU 上合成有限状态转换器

Composing Finite State Transducers on GPUs

We introduce a parallel GPU implementation of the Simple Linear Iterative
Clustering (SLIC) superpixel segmentation. Using a single graphic card, our
implementation achieves speedups of up to $83\times$ from the standard
sequential implementation. Our implementation is fully compatible with the
standard sequential implementation and the software is now available online and
is open source.

本文介绍了一个基于 GPU 并行计算的超像素分割方法，使用单个显卡可将标准顺序实现的速度提高 83 倍，同时兼容标准顺序实现且代码开源。