Sparse Matrix Belief Propagation

We propose sparse-matrix belief propagation, which executes loopy belief propagation in Markov random fields by replacing indexing over graph neighborhoods with sparse-matrix operations. This abstraction allows for seamless integration with optimized sparse linear algebra libraries, including those that perform matrix and tensor operations on modern hardware such as graphical processing units (GPUs). The sparse-matrix abstraction allows the implementation of belief propagation in a high-level language (e.g., Python) that is also able to leverage the power of GPU parallelization. We demonstrate sparse-matrix belief propagation by implementing it in a modern deep learning framework (PyTorch), measuring the resulting massive improvement in running time, and facilitating future integration into deep learning models.

Name thesis:degree_name

Master of Science

Level thesis:degree_level

masters

Discipline thesis:degree_discipline

Computer Science and Applications

Department dc:contributor.department

Computer Science

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2018

Author dc:creator

• Bixler, Reid Morris

Chair dc:contributor.committeechair

• Huang, Bert

Committee members dc:contributor.committeemember

• Wang, Gang Alan
• Huang, Jia-Bin

• belief propagation
• inference
• GPU
• sparse matrix

Statement dc:rights

• In Copyright

Licence dc:rights.uri

• http://rightsstatements.org/vocab/InC/1.0/

Dc Identifier Other

vt_gsexam:15270

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/83228