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arXiv · 1909.11469

Message Scheduling for Performant, Many-Core Belief Propagation

Mark Van der Merwe·Vinu Joseph·Ganesh Gopalakrishnan
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Abstract

Belief Propagation (BP) is a message-passing algorithm for approximate inference over Probabilistic Graphical Models (PGMs), finding many applications such as computer vision, error-correcting codes, and protein-folding. While general, the convergence and speed of the algorithm has limited its practical use on difficult inference problems. As an algorithm that is highly amenable to parallelization, many-core Graphical Processing Units (GPUs) could significantly improve BP performance. Improving BP through many-core systems is non-trivial: the scheduling of messages in the algorithm strongly affects performance. We present a study of message scheduling for BP on GPUs. We demonstrate that BP exhibits a tradeoff between speed and convergence based on parallelism and show that existing message schedulings are not able to utilize this tradeoff. To this end, we present a novel randomized message scheduling approach, Randomized BP (RnBP), which outperforms existing methods on the GPU.

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Mark Van der Merwe, Vinu Joseph, Ganesh Gopalakrishnan. 2019-09-24. Message Scheduling for Performant, Many-Core Belief Propagation. https://arxiv.org/abs/1909.11469

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