An Incremental Compilation Approach for OpenMP Applications

An approach to incremental compilation

ACM SIGPLAN Notices ◽

10.1145/502949.502889 ◽

1984 ◽

Vol 19 (6) ◽

pp. 144-156 ◽

Cited By ~ 8

Author(s):

Steven P. Reiss

Keyword(s):

Incremental Compilation

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Incremental compilation in Magpie

ACM SIGPLAN Notices ◽

10.1145/502949.502887 ◽

1984 ◽

Vol 19 (6) ◽

pp. 122-131 ◽

Cited By ~ 13

Author(s):

Mayer D. Schwartz ◽

Norman M. Delisle ◽

Vimal S. Begwani

Keyword(s):

Incremental Compilation

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Incremental compilation and conversational interpretation

Annual Review in Automatic Programming ◽

10.1016/0066-4138(73)90002-5 ◽

1973 ◽

Vol 7 ◽

pp. 95-114 ◽

Cited By ~ 3

Author(s):

M Berthaud ◽

M Griffiths

Keyword(s):

Incremental Compilation

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INCREMENTAL COMPILATION OF BAYESIAN NETWORKS BASED ON MAXIMAL PRIME SUBGRAPHS

International Journal of Uncertainty Fuzziness and Knowledge-Based Systems ◽

10.1142/s0218488511006952 ◽

2011 ◽

Vol 19 (02) ◽

pp. 155-191 ◽

Cited By ~ 5

Author(s):

M. JULIA FLORES ◽

JOSE A. GÁMEZ ◽

KRISTIAN G. OLESEN

Keyword(s):

Bayesian Networks ◽

Bayesian Network ◽

Great Improvement ◽

Experimental Evaluation ◽

Iterative Process ◽

Dynamic Models ◽

Probability Distributions ◽

Probability Propagation ◽

Incremental Compilation

When a Bayesian network (BN) is modified, for example adding or deleting a node, or changing the probability distributions, we usually will need a total recompilation of the model, despite feeling that a partial (re)compilation could have been enough. Especially when considering dynamic models, in which variables are added and removed very frequently, these recompilations are quite resource consuming. But even further, for the task of building a model, which is in many occasions an iterative process, there is a clear lack of flexibility. When we use the term Incremental Compilation or IC we refer to the possibility of modifying a network and avoiding a complete recompilation to obtain the new (and different) join tree (JT). The main point we intend to study in this work is JT-based inference in Bayesian networks. Apart from undertaking the triangulation problem itself, we have achieved a great improvement for the compilation in BNs. We do not develop a new architecture for BNs inference, but taking some already existing framework JT-based for probability propagation such as Hugin or Shenoy and Shafer, we have designed a method that can be successfully applied to get better performance, as the experimental evaluation will show.

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