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Running MAP Inference on Million Node Graphical Models: A High Performance Computing Perspective | IEEE Conference Publication | IEEE Xplore

Running MAP Inference on Million Node Graphical Models: A High Performance Computing Perspective


Abstract:

An important problem in discrete graphical models is the maximum a posterior (MAP) inference problem. Recent research has been focusing on the development of parallel MAP...Show More

Abstract:

An important problem in discrete graphical models is the maximum a posterior (MAP) inference problem. Recent research has been focusing on the development of parallel MAP inference algorithm, which scales to graphical models of millions of nodes. In this paper, we introduce a parallel implementation of the recently proposed Bethe-ADMM algorithm using Message Passing Interface (MPI), which allows us to fully utilize the computing power provided by the modern supercomputers with thousands of cores. Experimental results demonstrate that for a broad class of problems, our parallel implementation of Bethe-ADMM scales almost linearly even with thousands of cores.
Date of Conference: 04-07 May 2015
Date Added to IEEE Xplore: 09 July 2015
Electronic ISBN:978-1-4799-8006-2
Conference Location: Shenzhen, China

References

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