An introduction to parallel algorithms
An introduction to parallel algorithms
Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
Bayesian Networks for Data Mining
Data Mining and Knowledge Discovery
A parallel Lauritzen-Spiegelhalter algorithm for probabilistic inference
Proceedings of the 1994 ACM/IEEE conference on Supercomputing
Dynamic, Competitive Scheduling of Multiple DAGs in a Distributed Heterogeneous Environment
HCW '98 Proceedings of the Seventh Heterogeneous Computing Workshop
The potential of the cell processor for scientific computing
Proceedings of the 3rd conference on Computing frontiers
Scalable Parallel Implementation of Exact Inference in Bayesian Networks
ICPADS '06 Proceedings of the 12th International Conference on Parallel and Distributed Systems - Volume 1
Bayesian model learning based on a parallel MCMC strategy
Statistics and Computing
CellSs: a programming model for the cell BE architecture
Proceedings of the 2006 ACM/IEEE conference on Supercomputing
FFTC: fastest Fourier transform for the IBM cell broadband engine
HiPC'07 Proceedings of the 14th international conference on High performance computing
Logarithmic time parallel Bayesian inference
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Parallel Evidence Propagation on Multicore Processors
PaCT '09 Proceedings of the 10th International Conference on Parallel Computing Technologies
Parallel exact inference on the Cell Broadband Engine processor
Journal of Parallel and Distributed Computing
Parallelizing a convergent approximate inference method
Canadian AI'11 Proceedings of the 24th Canadian conference on Advances in artificial intelligence
Resource-aware junction trees for efficient multi-agent coordination
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Parallel evidence propagation on multicore processors
The Journal of Supercomputing
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We present the design and implementation of a parallel exact inference algorithm on the Cell Broadband Engine (Cell BE). Exact inference is a key problem in exploring probabilistic graphical models. In such a model, the computation complexity increases dramatically with the network structure and clique size. In this paper, we exploit parallelism at multiple levels. We present an efficient scheduler to dynamically partition large tasks and allocate synergistic processing elements (SPEs). We explore potential table representation and data layout to optimize DMA transfer between the local store and main memory. We also optimized the computation kernels. We achieved linear speedup and superior performance, compared with state-of-the-art processors such as the AMD Opteron, Intel Xeon and Pentium 4. The methodology proposed in this paper can be used for online scheduling of directed acyclic graph (DAG) structured computations.