Scalable Node-Level Computation Kernels for Parallel Exact Inference
In this paper, we investigate data parallelism in exact inference with respect to arbitrary junction trees. Exact inference is a key problem in exploring probabilistic graphical models, where the computation complexity increases dramatically with clique width and the number of states of random varia...
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| Veröffentlicht in: | IEEE transactions on computers Jg. 59; H. 1; S. 103 - 115 |
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01.01.2010
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| Abstract | In this paper, we investigate data parallelism in exact inference with respect to arbitrary junction trees. Exact inference is a key problem in exploring probabilistic graphical models, where the computation complexity increases dramatically with clique width and the number of states of random variables. We study potential table representation and scalable algorithms for node-level primitives. Based on such node-level primitives, we propose computation kernels for evidence collection and evidence distribution. A data parallel algorithm for exact inference is presented using the proposed computation kernels. We analyze the scalability of node-level primitives, computation kernels, and the exact inference algorithm using the coarse-grained multicomputer (CGM) model. According to the analysis, we achieve O(Nd c w c Pi j=1 wc r C,j /P) local computation time and O(N) global communication rounds using P processors, 1 les P les max c PiPi j1 wc r C,j, where N is the number of cliques in the junction tree; d c is the clique degree; r C,j is the number of states of the jth random variable in C; wc is the clique width; and w s is the separator width. We implemented the proposed algorithm on state-of-the-art clusters. Experimental results show that the proposed algorithm exhibits almost linear scalability over a wide range. |
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| AbstractList | In this paper, we investigate data parallelism in exact inference with respect to arbitrary junction trees. Exact inference is a key problem in exploring probabilistic graphical models, where the computation complexity increases dramatically with clique width and the number of states of random variables. We study potential table representation and scalable algorithms for node-level primitives. Based on such node-level primitives, we propose computation kernels for evidence collection and evidence distribution. A data parallel algorithm for exact inference is presented using the proposed computation kernels. We analyze the scalability of node-level primitives, computation kernels, and the exact inference algorithm using the coarse-grained multicomputer (CGM) model. According to the analysis, we achieve O(Nd c w c Pi j=1 wc r C,j /P) local computation time and O(N) global communication rounds using P processors, 1 les P les max c PiPi j1 wc r C,j, where N is the number of cliques in the junction tree; d c is the clique degree; r C,j is the number of states of the jth random variable in C; wc is the clique width; and w s is the separator width. We implemented the proposed algorithm on state-of-the-art clusters. Experimental results show that the proposed algorithm exhibits almost linear scalability over a wide range. |
| Author | Prasanna, V.K. Yinglong Xia |
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| Cites_doi | 10.1109/SUPERC.1994.344295 10.1109/ICPADS.2006.96 10.1016/B978-1-55860-332-5.50070-5 10.1111/j.2517-6161.1988.tb01721.x 10.1093/bioinformatics/17.suppl_1.S243 10.1016/j.parco.2007.11.004 10.1016/B978-0-444-88071-0.50023-0 10.1109/71.605771 10.1109/IPDPS.2008.4536192 10.1023/A:1009730122752 10.1109/5.92042 10.1109/SBAC-PAD.2007.18 10.1177/0037549707084939 10.1177/1094342005051196 10.1007/s11227-006-0005-4 10.1016/j.jcss.2007.02.007 10.1007/s11227-006-0009-0 10.1109/IPDPS.2007.370556 10.1016/S0888-613X(96)00069-2 10.1109/ICPADS.2006.97 10.1109/TC.2007.1068 |
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| StartPage | 103 |
| SubjectTerms | Bayesian network Computational modeling Exact inference Inference algorithms junction tree Junctions message passing node-level primitives Particle separators Program processors Random variables |
| Title | Scalable Node-Level Computation Kernels for Parallel Exact Inference |
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