Parallel Algorithms for GPU accelerated Probabilistic Inference
Loading...
Date
2012-02-21
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Abstract
Real world data is likely to contain an inherent structure. Those structures may be
represented with graphs which encode independence assumptions within the data.
Performing inference in those models is nearly intractable on mobile devices or
casual workstations. This work introduces and compares two approaches for ac-
celerating the inference in graphical models by using GPUs as parallel processing
units. It is empirically showed, that in order to achieve a scaleable parallel algo-
rithm, one has to distribute the workload equally among all processing units of a
GPU. We accomplished this by introducing Thread-Cooperative message compu-
tations.