Offline library adaptation using automatically generated heuristics

Proceedings of the 2010 IEEE International Symposium on Parallel and Distributed Processing, IPDPS 2010

Volumn , Issue , 2010, Pages

  De Mesmay, Frédéric ^a   Voronenko, Yevgen ^a   Püschel, Markus ^a  

^a CARNEGIE MELLON UNIVERSITY   (United States)

Author keywords

Automatic performance tuning; Decision trees; Fast Fourier transform; FFT; High performance computing; Library generation; Machine learning; Statistical classifier

Indexed keywords

AUTOMATIC PERFORMANCE TUNING; HIGH-PERFORMANCE COMPUTING; LIBRARY GENERATION; MACHINE-LEARNING; STATISTICAL CLASSIFIER;

CLASSIFIERS; COMPUTER ARCHITECTURE; DISTRIBUTED PARAMETER NETWORKS; FAST FOURIER TRANSFORMS; LEARNING SYSTEMS; LIBRARIES; TUNING; WAVELET TRANSFORMS;

DECISION TREES;

EID: 77953987795     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IPDPS.2010.5470479     Document Type: Conference Paper

References (19)

1. Intel, "Integrated Performance Primitives (IPP) 6.0, User Guide."
2. R. C. Whaley and J. Dongarra, "Automatically Tuned Linear Algebra Software (ATLAS)," in Proc. Supercomputing, 1998.
3. M. Frigo and S. G. Johnson, "The design and implementation of FFTW3," Proceedings of the IEEE, vol.93, no.2, pp. 216-231, 2005.
4. Y. Voronenko, F. de Mesmay, and M. Püschel, "Computer generation of general size linear transform libraries," in Proc. Int'l Symp. on Code Generation and Optimization (CGO), 2009, pp. 102-113.
5. F. de Mesmay et al., "Bandit-based optimization on graphs with application to library performance tuning," in Proc. Int'l Conf. on Machine Learning (ICML), 2009, pp. 729-736.
6. J. R. Quinlan, C4.5: programs for machine learning. Morgan Kaufmann Publishers Inc., 1993.
7. B. Singer and M. Veloso, "Learning to construct fast signal processing implementations," Journal of Machine Learning Research, special issue on ICML 2001, vol.3, pp. 887-919, 2002.
8. K. D. Cooper et al., "ACME: adaptive compilation made efficient," SIGPLAN Not., vol.40, no.7, pp. 69-77, 2005.
9. Z. Pan and R. Eigenmann, "Fast and effective orchestration of compiler optimizations for automatic performance tuning," in Proc. Int'l Symp. on Code Generation and Optimization (CGO), 2006, pp. 319-332.
10. K. D. Cooper, P. J. Schielke, and D. Subramanian, "Optimizing for reduced code space using genetic algorithms," in Proc. Languages, Compilers, and Tools for Embedded Systems (LCTES). ACM, 1999, pp. 1-9.
11. E. Moss et al., "Learning to schedule straight-line code," in Proc. Advances in Neural Information Processing Systems (NIPS) '97, 1997, pp. 929-935.
12. A. Monsifrot, F. Bodin, and R. Quiniou, "A machine learning approach to automatic production of compiler heuristics," in Artificial Intelligence: Methodology, Systems, Applications. Springer, 2002, pp. 41-50.
13. J. Cavazos and E. Moss, "Inducing heuristics to decide whether to schedule," SIGPLAN Not., vol.39, no.6, pp. 183-194, 2004.
14. B. Calder et al., "Evidence-based static branch prediction using machine learning," ACM Trans. Program. Lang. Syst., vol.19, no.1, pp. 188-222, 1997.
15. M. Stephenson et al., "Meta optimization: improving compiler heuristics with machine learning," in Proc. Conf. on Programming Language Design and Implementation (PLDI), 2003, pp. 77-90.
16. M. Stephenson and S. Amarasinghe, "Predicting unroll factors using supervised classification," in Proc. Int'l Symp. on Code Generation and Optimization (CGO), 2005, pp. 123-134.
17. H. Leather, E. Bonilla, and M. O'Boyle, "Automatic feature generation for machine learning based optimizing compilation," in Proc. Int'l Symp. on Code Generation and Optimization (CGO), 2009, pp. 81-91.
18. C. E. Shannon, "A mathematical theory of communication," Bell system technical journal, vol.27, 1948.
19. M. Püschel et al., "SPIRAL: Code generation for DSP transforms," Proceedings of the IEEE, vol.93, no.2, pp. 232-275, 2005.