Abstract
Manually tuning MPI runtime parameters is a practice commonly employed to optimise MPI application performance on a specific architecture. However, the best setting for these parameters not only depends on the underlying system but also on the application itself and its input data. This paper introduces a novel approach based on machine learning techniques to estimate the values of MPI runtime parameters that tries to achieve optimal speedup for a target architecture and any unseen input program. The effectiveness of our optimization tool is evaluated against two benchmarks executed on a multi-core SMP machine.
This work is funded by the Tiroler Zukunftsstiftung under contract nr. P7030-015-024.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Open MPI: Modular Component Architecture, http://www.open-mpi.org
Bodin, F., Kisuki, T., Knijnenburg, P., O’Boyle, M., Rohou, E.: Iterative Compilation in a Non-Linear Optimisation Space. In: Proceedings of the Workshop on Profile Directed Feedback-Compilation (October 1998)
Chaarawi, M., Squyres, J.M., Gabriel, E., Feki, S.: A tool for optimizing runtime parameters of open mpi. In: Lastovetsky, A., Kechadi, T., Dongarra, J. (eds.) EuroPVM/MPI 2008. LNCS, vol. 5205, pp. 210–217. Springer, Heidelberg (2008)
Chai, L., Lai, P., Jin, H.W., Panda, D.K.: Designing an efficient kernel-level and user-level hybrid approach for mpi intra-node communication on multi-core systems. In: ICPP 2008: Proceedings of the 2008 37th International Conference on Parallel Processing, Washington, DC, USA. IEEE Computer Society Press, Los Alamitos (2008)
Meijerink, J.A., Vorst, H.A.v.d.: An iterative solution method for linear systems of which the coefficient matrix is a symmetric m-matrix. Mathematics of Computation 31(137), 148–162 (1977)
der Wijngaart, R.F.V.: Nas Parallel Benchmarks Version 2.4. Technical Report NAS-02-007, Computer Science Corporation NASA Advanced Supercomputing (NAS) Division (October 2002)
Ian, H., Witten, E.F.: Data Mining: Practical Machine Learning Tools and Techniques. Elsevier, Amsterdam (2005)
Bishop, C.M.: Neural Networks for Pattern Recognition. Oxford University Press, Oxford (1996)
Ipek, E., Supinski, B.R., Schulz, M., Mckee, S.A.: An Approach to Performance Prediction for Parallel Applications. In: Euro-Par Parallel Processing (2005)
Matlab: Neural Network Toolbox, http://www.mathworks.com/products/neuralnet/
Faraj, A., Yuan, X., Lowenthal, D.: Star-mpi: self tuned adaptive routines for mpi collective operations. In: ICS 2006: Proceedings of the 20th annual international conference on Supercomputing, pp. 199–208. ACM, New York (2006)
Fagg, G.E., Angskun, T., Bosilca, G., Dongarra, J.J.: Decision trees and mpi collective algorithm selection problem (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Pellegrini, S., Wang, J., Fahringer, T., Moritsch, H. (2009). Optimizing MPI Runtime Parameter Settings by Using Machine Learning. In: Ropo, M., Westerholm, J., Dongarra, J. (eds) Recent Advances in Parallel Virtual Machine and Message Passing Interface. EuroPVM/MPI 2009. Lecture Notes in Computer Science, vol 5759. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03770-2_26
Download citation
DOI: https://doi.org/10.1007/978-3-642-03770-2_26
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-03769-6
Online ISBN: 978-3-642-03770-2
eBook Packages: Computer ScienceComputer Science (R0)