Running MAP Inference on Million Node Graphical Models: A High Performance Computing Perspective
Chen Jin, Qiang Fu, Huahua Wang, William Hendrix, Zhengzhang Chen, Ankit Agrawal, Arindam Banerjee, Alok N. Choudhary · 2015
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.