Parallel Inference on Structured Data with CRFs on GPUs
Loading...
Date
2012-02-21
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Abstract
Structured real world data can be represented with graphs
whose structure encodes independence assumptions within the data. Due
to statistical advantages over generative graphical models, Conditional
Random Fields (CRFs) are used in a wide range of classification tasks on
structured data sets. CRFs can be learned from both, fully or partially
supervised data, and may be used to infer fully unlabeled or partially
labelled data. However, performing inference in CRFs with an arbitrary
graphical structure on a large amount of data is computational expensive and nearly intractable on a reseacher's workstation. Hence, we take
advantage of recent developments in computer hardware, namely general-
purpose Graphics Processing Units (GPUs). We not merely run given algorithms on GPUs, but present a novel framework of parallel algorithms
at several levels for training general CRFs on very large data sets. We
evaluate their performance in terms of runtime and F1-Score.