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APIN
1999
107views more  APIN 1999»
13 years 4 months ago
Massively Parallel Probabilistic Reasoning with Boltzmann Machines
We present a method for mapping a given Bayesian network to a Boltzmann machine architecture, in the sense that the the updating process of the resulting Boltzmann machine model pr...
Petri Myllymäki
SC
1993
ACM
13 years 9 months ago
Solving the Boltzmann equation at 61 gigaflops on a 1024-node CM-5
This paper documents the use of a massively parallel computer, specifically the Connection Machine CM-5, to solve the Boltzmann equation to model one-dimensional shock wave struct...
Lyle N. Long, Jacek Myczkowski
CLUSTER
2008
IEEE
13 years 5 months ago
Benchmarking the effects of operating system interference on extreme-scale parallel machines
We investigate operating system noise, which we identify as one of the main reasons for a lack of synchronicity in parallel applications. Using a microbenchmark, we measure the no...
Peter H. Beckman, Kamil Iskra, Kazutomo Yoshii, Su...
PVLDB
2008
182views more  PVLDB 2008»
13 years 4 months ago
SCOPE: easy and efficient parallel processing of massive data sets
Companies providing cloud-scale services have an increasing need to store and analyze massive data sets such as search logs and click streams. For cost and performance reasons, pr...
Ronnie Chaiken, Bob Jenkins, Per-Åke Larson,...
JMLR
2010
145views more  JMLR 2010»
12 years 11 months ago
Parallelizable Sampling of Markov Random Fields
Markov Random Fields (MRFs) are an important class of probabilistic models which are used for density estimation, classification, denoising, and for constructing Deep Belief Netwo...
James Martens, Ilya Sutskever