Publikation: DAG Mining for Code Compaction
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Datum
2009
Autor:innen
Werth, Tobias
Wörlein, Marc
Dreweke, Alexander
Fischer, Ingrid
Philippsen, Michael
Herausgeber:innen
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CAO, Longbing, ed. and others. Data Mining for Business Applications. New York: Springer, 2009, pp. 209-223
Zusammenfassung
In order to reduce cost and energy consumption, code-size optimization is an important issue for embedded systems. Traditional instruction saving techniques recognize code duplications only in exactly the same order within the program. As instructions can be reordered with respect to their data dependencies, Procedural Abstraction achieves better results on data flow graphs that reflect these dependencies. Since these graphs are always directed acyclic graphs (DAGs), a special mining algorithm for DAGs is presented in this chapter. Using a new canonical representation that is based on the topological order of the nodes in a DAG, the proposed algorithm is faster and uses less memory than the general graph mining algorithm gSpan. Due to its search lattice expansion strategy, an efficient pruning strategy is applied to the algorithm while using it for Procedural Abstraction. Its search for unconnected graph fragments outperforms traditional approaches for code-size reduction.
Zusammenfassung in einer weiteren Sprache
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004 Informatik
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Azyklischer gerichteter Graph, Data Mining
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WERTH, Tobias, Marc WÖRLEIN, Alexander DREWEKE, Ingrid FISCHER, Michael PHILIPPSEN, 2009. DAG Mining for Code Compaction. In: CAO, Longbing, ed. and others. Data Mining for Business Applications. New York: Springer, 2009, pp. 209-223BibTex
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booktitle={Data Mining for Business Applications},
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<dcterms:abstract xml:lang="eng">In order to reduce cost and energy consumption, code-size optimization is an important issue for embedded systems. Traditional instruction saving techniques recognize code duplications only in exactly the same order within the program. As instructions can be reordered with respect to their data dependencies, Procedural Abstraction achieves better results on data flow graphs that reflect these dependencies. Since these graphs are always directed acyclic graphs (DAGs), a special mining algorithm for DAGs is presented in this chapter. Using a new canonical representation that is based on the topological order of the nodes in a DAG, the proposed algorithm is faster and uses less memory than the general graph mining algorithm gSpan. Due to its search lattice expansion strategy, an efficient pruning strategy is applied to the algorithm while using it for Procedural Abstraction. Its search for unconnected graph fragments outperforms traditional approaches for code-size reduction.</dcterms:abstract>
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