High-throughput Bayesian network learning using heterogeneous multicore computers

Proceedings of the International Conference on Supercomputing

Volumn , Issue , 2010, Pages 95-104

  Linderman, Michael D ^a   Bruggner, Robert ^b   Athalye, Vivek ^a   Meng, Teresa H ^a   Bani Asadi, Narges ^a   Nolan, Garry P ^b  

^a Stanford University   (United States)

^b STANFORD UNIVERSITY   (United States)

Author keywords

Bayesian networks; GPU; MCMC

Indexed keywords

BAYESIAN NETWORK LEARNING; BAYESIAN NETWORKS (BNS); EXPERIMENTAL DATA; GENERAL PURPOSE PROCESSORS; GRAPHICS PROCESSING UNIT; HETEROGENEOUS MULTICORE; HETEROGENEOUS SYSTEMS; HIGH-THROUGHPUT; INTRACELLULAR SIGNALING; MONTE CARLO MARKOV CHAIN; MULTI CORE; NP-HARD PROBLEM; PROGRAMMING MODELS;

COMPUTER GRAPHICS EQUIPMENT; DISTRIBUTED PARAMETER NETWORKS; GENERAL PURPOSE COMPUTERS; INFERENCE ENGINES; INTELLIGENT CONTROL; INTELLIGENT NETWORKS; LEARNING ALGORITHMS; MARKOV PROCESSES; PROGRAM PROCESSORS; SECURITY OF DATA; SIGNAL TRANSDUCTION; SPEECH RECOGNITION;

BAYESIAN NETWORKS;

EID: 77954717001     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1810085.1810101     Document Type: Conference Paper

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