Zusammenfassung
There is an explosion of data, documents, and other content, and people
require tools to analyze and interpret these, tools to turn the content into
information and knowledge. Topic modeling have been developed to solve these
problems. Topic models such as LDA Blei et. al. 2003 allow salient patterns
in data to be extracted automatically. When analyzing texts, these patterns are
called topics. Among numerous extensions of LDA, few of them can reliably
analyze multiple groups of documents and extract topic similarities. Recently,
the introduction of differential topic modeling (SPDP) Chen et. al. 2012
performs uniformly better than many topic models in a discriminative setting.
There is also a need to improve the sampling speed for topic models. While
some effort has been made for distributed algorithms, there is no work
currently done using graphical processing units (GPU). Note the GPU framework
has already become the most cost-efficient platform for many problems.
In this thesis, I propose and implement a scalable multi-GPU distributed
parallel framework which approximates SPDP. Through experiments, I have shown
my algorithms have a gain in speed of about 50 times while being almost as
accurate, with only one single cheap laptop GPU. Furthermore, I have shown the
speed improvement is sublinearly scalable when multiple GPUs are used, while
fairly maintaining the accuracy. Therefore on a medium-sized GPU cluster, the
speed improvement could potentially reach a factor of a thousand.
Note SPDP is just a representative of other extensions of LDA. Although my
algorithm is implemented to work with SPDP, it is designed to be a general
enough to work with other topic models. The speed-up on smaller collections
(i.e., 1000s of documents), means that these more complex LDA extensions could
now be done in real-time, thus opening up a new way of using these LDA models
in industry.
Nutzer