Predicting parallel application performance via machine learning approaches

Concurrency and Computation: Practice and Experience

Volumn 19, Issue 17, 2007, Pages 2219-2235

  Singh, Karan ^a   Ipek, Engin ^a   McKee, Sally A ^a,c   De Supinski, Bronis R ^b   Schulz, Martin ^b   Caruana, Rich ^a  

^a Cornell University ^*  (United States)

^b LAWRENCE LIVERMORE NATIONAL LABORATORY   (United States)

^c Cornell University   (United States)

Author keywords

Artificial neural networks; High performance computing; Performance modeling

Indexed keywords

COMPUTATIONAL COMPLEXITY; MATHEMATICAL MODELS; NEURAL NETWORKS; PARAMETER ESTIMATION; ROBUST CONTROL;

ARCHITECTURAL COMPLEXITY; HIGH PERFORMANCE COMPUTING; PERFORMANCE MODELING;

LEARNING SYSTEMS;

EID: 35948986416     PISSN: 15320626     EISSN: 15320634     Source Type: Journal    
DOI: 10.1002/cpe.1171     Document Type: Article

References (17)

1. Falgout R, Yang U. hypre: A library of high performance preconditioners. Proceedings of the International Conference on Computational Science (ICCS), Part III, April 2002 (Lecture Notes in Computer Science, vol. 2331). Springer: Berlin, 2002; 632-641.
2. The ASCI Purple Benchmark Codes, October 2002. http://www.llnl.gov/asci/ purple/rfp/benchmarks/limited/code_list.html [22 January 2007].
3. Petitet A, Whaley R, Dongarra J, Cleary A. HPL - A portable implementation of the High-Performance Linpack benchmark for distributed-memory computers, March 2006. http://www.netlib.org/benchmark/hpl/ [22 January 2007].
4. Ìpek E, McKee S, de Supinski B, Schulz M, Caruana R. Efficiently exploring architectural design spaces via predictive modeling. Proceedings of the Architectural Support for Programming Languages and Operating Systems (ASPLOS XII), October 2006. ACM Press: New York, 2006.
5. Brown P, Falgout R, Jones J. Semicoarsening multigrid on distributed memory machines. SIAM Journal on Scientific Computing 2000; 21:1823-1834.
6. Riedmiller M, Braun H. A direct adaptive method for faster backpropagation learning: The RPROP algorithm. Proceedings of the IEEE International Conference on Neural Networks, May 1993. IEEE Computer Society Press: Los Alamitos, CA, 1993; 586-591.
7. Saar-Tsechansky M, Provost F. Active learning for class probability estimation and ranking. Proceedings International Joint Conference on Artificial Intelligence (IJCAI), August 2001. AAAI Press: Cambridge MA, 2001; 911-920.
8. Breiman L. Bagging predictors. Machine Learning 1996; 24(2):123-140.
9. Ìpek E, de Supinski B, Schulz M, McKee S. An approach to performance prediction for parallel applications. Proceedings Euro-Par, August 2005 (Lecture Notes in Computer Science, vol. 3648). Springer: Berlin, 2005; 196-205.
10. Mitchell T. Machine Learning. WCB/McGraw-Hill: New York, 1997.
11. Caruana R, Lawrence S, Giles C. Overfitting in neural nets: Backpropagation, conjugate gradient, and early stopping. Proceedings of the Neural Information Processing Systems (NIPS), November 2000. MIT Press: Cambridge, MA, 2000; 402-408.
12. Almási G. Design and implementation of message-passing services for the Blue Gene/L supercomputer. IBM Journal of Research and Devevelopment 2005; 49(2-3):393-406.
13. Marin G, Mellor-Crummey J. Cross-architecture performance predictions for scientific applications using parameterized models. Proceedings of the International Conference on Measurement and Modeling of Computer Systems (SIGMETRICS), June 2004. ACM Press: New York, 2004; 2-13.
14. Carrington L, Snavely A, Gao X, Wolter N. A performance prediction framework for scientific applications. Proceedings of the International Conference on Computational Science Workshop on Performance Modeling and Analysis (PMA03), June 2003 (Lecture Notes in Computer Science, vol. 2659). Springer: Berlin, 2003; 926-935.
15. Carrington L, Wolter N, Snavely A, Lee C. Applying an automatic framework to produce accurate blind performance predictions of full-scale HPC applications. Proceedings of the Department of Defense Users Group Conference, June 2004. Department of Defense: Washington, DC, 2004.
16. Kerbyson D, Alme H, Hoisie A, Petrini F, Wasserman H, Gittings M. Predictive performance and scalability modeling of a large-scale application. Proceedings of the IEEE/ACM Supercomputing Conference, November 2001. IEEE Computer Society Press: Los Alamitos, CA, 2001.
17. Yang T, Ma X, Mueller F. Cross-platform performance prediction of parallel applications using partial execution. Proceedings of IEEE/ACM Supercomputing, November 2005. IEEE Computer Society Press: Los Alamitos, CA, 2005.