Abstract
Since chip multiprocessors are quickly penetrating new application areas in network and media processing, their interconnection architectures become a subject of sophisticated optimization. One-to-All Broadcast (OAB) and All-to-All Broadcast (AAB) [2] group communications are frequently used in many parallel algorithms and if their overhead cost is excessive, performance degrades rapidly with a processor count. This paper deals with the design of a new application-specific standard genetic algorithm (SGA) and the use of Hybrid parallel Genetic Simulated Annealing (HGSA) to design optimal communication algorithms for an arbitrary topology of the interconnection network. Each of these algorithms is targeted for a different switching technique. The OAB and AAB communication schedules were designed mainly for an asymmetrical AMP [15] network and for the benchmark hypercube network [16] using Store-and-Forward (SF) and Wormhole (WH) switching.
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
Jantsch, A., Tenhunen, H.: Networks on Chip. Kluwer Academic Publ., Boston (2003) ISBN 1-4020-7392-5
Gabrielyan, E., Hersch, R.D.: Network′s Liquid Throughput: Topology Aware Optimization of Collective Communication. Unpublished work (2003)
Goldberg, D.: Genetics Algorithms in Search, Optimization, and Machine Learning. Addision-Wesley Publishing Company, Reading (1989)
Defago, X., Schiper, A., Urban, P.: Total Order Broadcast and Multicast Algorithms: Taxonomy and Survey, technical report DSC/2000/036 (2003)
Duato, J., Yalamanchili, S.: Interconnection Networks – An Engineering Approach. Morgan Kaufman Publishers, Elsevier Science (2003)
Staroba, J.: Parallel Performance Modelling, Prediction and Tuning, PhD. Thesis. Brno University of Technology, Brno, Czech Rep. (2004)
Ohlídal, M., Schwarz, J.: Hybrid parallel simulated annealing using genetic operations. In: Mendel 2004 10th Internacional Conference on Soft Computing, Brno, CZ, FSI VUT, pp. 89–94 (2004)
Goldberg, D.E.: A note on Boltzmann tournament selection for genetic algorithms and population-oriented simulated annealing, Complex Systems, pp. 445–460 (1990)
Kita, H.: Simulated annealing. Proceeding of Japan Society for Fuzzy Theory and Systems 9(6) (1997)
Jaroš, J., Dvořák, V.: Speeding-up OAS and AAS Communication in Networking System on Chips. In: Proc. of 8th IEEE Workshop on Design and Diagnostic of Electronic Circuits and Systems, Sopron, HU, UWH, pp. 206–210 (2005)
Jaroš, J., Ohlídal, M., Dvořák, V.: Evolutionary Design of Group Communication Schedules for Interconnection Networks. In: Proceedings of 20th International Symposium of Computer and Information Science, Berlin, DE, pp. 472–481. Springer, Heidelberg (2005)
Dvořák, V.: Scheduling Collective Communications on Wormhole Fat Cubes. In: Proc. Of the 17th International Symposium on Computer Architecture and High Performance Computing, US. IEEE CS, Los Alamitos, pp. 27–34 (2005)
Pelikan, M., Goldberg, E., Sastry, K.: Bayesian Optimization Algorithm, Decision Graphs, and Occams Razor
Chalmers, A.-T.J.: Practical Parallel Processing. International Thomson Computer Press (1996)
http://www.sgi.com/products/remarketed/origin/pdf/hypercube.pdf
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ohlídal, M., Jaroš, J., Schwarz, J., Dvořák, V. (2006). Evolutionary Design of OAB and AAB Communication Schedules for Interconnection Networks. In: Rothlauf, F., et al. Applications of Evolutionary Computing. EvoWorkshops 2006. Lecture Notes in Computer Science, vol 3907. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11732242_24
Download citation
DOI: https://doi.org/10.1007/11732242_24
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-33237-4
Online ISBN: 978-3-540-33238-1
eBook Packages: Computer ScienceComputer Science (R0)