Difference between revisions of "GP (Graph Programs)"

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(Possible Research Projects)
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* '''Static analysis of graph programs.''' This project will develop an automatic program analysis for detecting confluence and termination in graph programs. A nondeterministic program is confluent (or 'don't care' nondeterministic) if all its executions on a given input will produce the same result. Confluence is essential for run time efficiency because GP's backtracking mechanism [1] can be turned off for confluent subprograms without compromising the semantics. Sufficient conditions for confluence will be developed by generalizing so-called critical pair techniques [4] from sets of graph transformation rules to programs. A simple static analysis of termination will complement the analysis of confluence.
 
* '''Static analysis of graph programs.''' This project will develop an automatic program analysis for detecting confluence and termination in graph programs. A nondeterministic program is confluent (or 'don't care' nondeterministic) if all its executions on a given input will produce the same result. Confluence is essential for run time efficiency because GP's backtracking mechanism [1] can be turned off for confluent subprograms without compromising the semantics. Sufficient conditions for confluence will be developed by generalizing so-called critical pair techniques [4] from sets of graph transformation rules to programs. A simple static analysis of termination will complement the analysis of confluence.
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More information about these possible projects (and others) are available on [[Prospective Research Students#Subject_Area:_Programming_by_Graph_Transformation_.28Detlef_Plump_.5B3.5D.29|our prospective research students page]].
  
 
[[Category:Graph Transformation]]
 
[[Category:Graph Transformation]]

Revision as of 19:14, 2 March 2010

GP (for Graph Programs) is a rule-based, nondeterministic programming language for solving graph problems at a high level of abstraction, freeing programmers from handling low-level data structures. The core of GP consists of four constructs: single-step application of a set of conditional graph-transformation rules, sequential composition, branching and iteration. The language has a structural operational semantics, as well as a prototype implementation.

Sandra Steinert, Greg Manning, and Detlef Plump worked on the design and implementation of GP; Chris Poskitt and Detlef Plump are currently investigating the formal verification of Graph Programs.

GP Literature

Overviews

  • Detlef Plump. The Graph Programming Language GP (.pdf). In Proc. Algebraic Informatics, Third International Conference (CAI 2009), volume 5725 of Lecture Notes in Computer Science, pages 99-122. Springer-Verlag, 2009.

Semantics

  • Detlef Plump and Sandra Steinert. The Semantics of Graph Programs (.pdf). In Proc. Rule-Based Programming (RULE 2009), Electronic Proceedings in Theoretical Computer Science, 2009. To appear.

Prototype Implementation

  • Greg Manning and Detlef Plump. The GP Programming System (.pdf). In Proc. Graph Transformation and Visual Modelling Techniques (GT-VMT 2008), volume 10 of Electronic Communications of the EASST, 2008.
  • Greg Manning and Detlef Plump. The York Abstract Machine (.pdf). In Proc. Graph Transformation and Visual Modelling Techniques (GT-VMT 2006), volume 211 of Electronic Notes in Theoretical Computer Science, pages 231-240. Elsevier, 2008.

Possible Research Projects

Please contact Detlef Plump for more information about the following possible Ph.D. projects.

  • Speeding up GP. This project will attempt to speed up GP's current implementation, which is based on a compiler and the York abstract machine [1,2], to rival with the fastest graph transformation tools currently available [3]. Activities to make GP faster include the introduction of a powerful type system for graphs to support the analysis of graphs at run time, the implementation of dynamic search plans for graph pattern matching, and the use of so-called rooted graph transformation rules which can be matched in constant time.
  • Static analysis of graph programs. This project will develop an automatic program analysis for detecting confluence and termination in graph programs. A nondeterministic program is confluent (or 'don't care' nondeterministic) if all its executions on a given input will produce the same result. Confluence is essential for run time efficiency because GP's backtracking mechanism [1] can be turned off for confluent subprograms without compromising the semantics. Sufficient conditions for confluence will be developed by generalizing so-called critical pair techniques [4] from sets of graph transformation rules to programs. A simple static analysis of termination will complement the analysis of confluence.

More information about these possible projects (and others) are available on our prospective research students page.