Abstract
A method is presented for evolving individuals that use an Attribute Grammar (AG) in a generative way. AGs are considerably more flexible and powerful than the closed, context free grammars normally employed by GP. Rather than evolving derivation trees as in most approaches, we employ a two step process that first generates a vector of real numbers using standard GP, before using the vector to produce a parse tree. As the parse tree is being produced, the choices in the grammar depend on the attributes being input to the current node of the parse tree. The motivation is automatic parallelization or the discovery of a re-factoring of a sequential code or equivalent parallel code that satisfies certain performance gains when implemented on a target parallel computing platform such as a multicore processor. An illustrative and a computed example demonstrate this methodology.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge (1992)
Howard, D., Roberts, S.C.: Genetic Programming solution of the convection-diffusion equation. In: Spector, L., et al. (eds.) Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2001), pp. 34–41. Morgan Kaufmann, San Francisco (2001)
Baber, C., Stanton, N., Howard, D., Houghton, R.J.: Paper 15 - Predicting the Structure of Covert Networks using Genetic Programming, Cognitive Work Analysis and Social Network Analysis. Papers presented at the NATO RTO Modelling and Simulation Group Symposium held in Brussels, Belgium on October 15-16, 2009 (2009) ISBN 978-92-837-0100-2
Howard, D.: Bio-inspired simulation tool for PERT. In: Lee, G., et al. (eds.) Proceedings of the 2009 International Conference on Hybrid Information Technology, ICHIT 2009, Daejeon, Korea, August 27-29. ACM International Conference Proceeding Series, vol. 321, pp. 537–540. ACM, New York (2009) ISBN 978-1-60558-662-5
Amdahl, G.: Validity of the Single Processor Approach to Achieving Large-Scale Computing Capabilities. In: AFIPS Conference Proceeding, vol. (30), pp. 483–485 (1967)
Ryan, C., Walsh, P.: Automatic conversion of programs from serial to parallel using Genetic Programming - The Paragen System. In: Proceedings of ParCo 1995. North-Holland, Amsterdam (1995)
Walsh, P., Ryan, C.: Paragen: A Novel Technique for the Autoparallelisation of Sequential Programs using Genetic Programming. In: Koza, J.R., et al. (eds.) Genetic Programming 1996: Proceedings of the First Annual Conference, pp. 406–409. Stanford University, MIT Press, CA, USA (1996)
Ryan, C., Laur, I.: Automatic Parallelization of Arbitrary Programs. In: Langdon, W.B., Fogarty, T.C., Nordin, P., Poli, R. (eds.) EuroGP 1999. LNCS, vol. 1598, pp. 244–254. Springer, Heidelberg (1999)
Ryan, C., Laur, I.: Paragen - The first results. In: Poli, R., Banzhaf, W., Langdon, W.B., Miller, J., Nordin, P., Fogarty, T.C. (eds.) EuroGP 2000. LNCS, vol. 1802, pp. 338–348. Springer, Heidelberg (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Howard, D., Ryan, C., Collins, J.J. (2011). Attribute Grammar Genetic Programming Algorithm for Automatic Code Parallelization. In: Lee, G., Howard, D., Ślęzak, D. (eds) Convergence and Hybrid Information Technology. ICHIT 2011. Lecture Notes in Computer Science, vol 6935. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24082-9_31
Download citation
DOI: https://doi.org/10.1007/978-3-642-24082-9_31
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24081-2
Online ISBN: 978-3-642-24082-9
eBook Packages: Computer ScienceComputer Science (R0)