Abstract
The detection and exploitation of different kinds of parallelism, task parallelism and data parallelism often leads to efficient parallel programs. This paper presents a simulation environment to predict the best mapping for the execution of message-passing applications on distributed systems. Using this environment, we evaluate the performance of an image processing application for the different parallelizing alternatives, and we propose the ways to improve its performance.
This work was supported by the MCyT under contract 2001-2592 and partially sponsored by the Generalitat de Catalunya (G. de Rec. Consolidat 2001SGR-00218).
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Rauber, T., Rünger, G.: Load Balancing Schemes for Extrapolation Methods. Concurrency. Practice and Experience 9(3), 181–202 (1997)
Subhlok, J., Vongran, G.: Optimal Use of Mixed Task and Data Parallelism for Pipelined Computations. J. Par. Distr. Computing 60, 297–319 (2000)
Guirado, F.: ESPPADA: Simulation Environment for Parallel Programs in Distributed Architectures. Master thesis (in Spanish). Computer Science Dep. Universidad Autónoma de Barcelona (2001)
Yuan, X., Roig, C., Ripoll, A., Senar, M.A., Guirado, F., Luque, E.: AMEEDA: A General-Purpose Mapping Tool for Parallel Applications on Dedicated Clusters. In: Monien, B., Feldmann, R.L. (eds.) Euro-Par 2002. LNCS, vol. 2400, pp. 248–252. Springer, Heidelberg (2002)
Roig, C., Ripoll, A., Senar, M.A., Guirado, F., Luque, E.: A New Model for Static Mapping of Parallel Applications with Task and Data Parallelism. In: IEEE Proc. of IPDPS-2002 Conf. (April 2002) ISBN: 0-7695-1573-8
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Guirado, F., Ripoll, A., Roig, C., Yuan, X., Luque, E. (2003). Predicting the Best Mapping for Efficient Exploitation of Task and Data Parallelism. In: Kosch, H., Böszörményi, L., Hellwagner, H. (eds) Euro-Par 2003 Parallel Processing. Euro-Par 2003. Lecture Notes in Computer Science, vol 2790. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45209-6_33
Download citation
DOI: https://doi.org/10.1007/978-3-540-45209-6_33
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-40788-1
Online ISBN: 978-3-540-45209-6
eBook Packages: Springer Book Archive