A Cross-Input Adaptive Framework for GPU Program Optimizations

IPDPS 2009 - Proceedings of the 2009 IEEE International Parallel and Distributed Processing Symposium

Volumn , Issue , 2009, Pages

  Liu, Yixun ^a   Zhang, Eddy Z ^a   Shen, Xipeng ^a  

^a William and Mary   (United States)

Author keywords

Cross input adaptation; CUDA; Empirical search; G ADAPT; GPU; Program optimizations

Indexed keywords

ADAPTIVE FRAMEWORK; ADAPTIVE OPTIMIZATION; GENERAL-PURPOSE COMPUTING; GPU PROGRAMMING; GPU PROGRAMS; GRAPHIC PROCESSING UNITS; HIGH QUALITY; INPUT ADAPTATION; NEW DIMENSIONS; NUMERICAL APPLICATIONS; OPTIMAL CONFIGURATIONS; PREDICTIVE MODELS; SINGLE-CHIP;

DISTRIBUTED PARAMETER NETWORKS; OPTIMIZATION; PARALLEL ARCHITECTURES;

COMPUTER GRAPHICS EQUIPMENT;

EID: 70450103746     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IPDPS.2009.5160988     Document Type: Conference Paper

References (25)

1. NVIDIA CUDA. http://www.nvidia.com/cuda.
2. M. Arnold, M. Hind, and B. G. Ryder. Online feedback-directed optimization of Java. In Proceedings of ACM Conference on Object- Oriented Programming Systems, Languages and Applications, pages 111-129, 2002.
3. M. M. Baskaran, U. Bondhugula, S. Krishnamoorthy, J. Ramanujam, A. Rountev, and P. Sadayappan. A compiler framework for optimization of affine loop nests for GPGPUs. In ICS'08: Proceedings of the 22nd Annual International Conference on Supercomputing, pages 225-234, 2008.
4. J. Bilmes, K. Asanovic, C.-W. Chin, and J. Demmel. Optimizing matrixmultiply using PHiPAC: A portable, high-performance, ANSI C coding methodology. In Proceedings of the ACM International Conference on Supercomputing, pages 340-347, 1997.
5. K. D. Cooper, M. W. Hall, and K. Kennedy. Procedure cloning. In Computer Languages, pages 96-105, 1992.
6. P. Diniz and M. Rinard. Dynamic feedback: an effective technique for adaptive computing. In Proceedings of ACM SIGPLAN Conference on Programming Language Design and Implementation, pages 71-84, Las Vegas, May 1997.
7. Y. Dotsenko, N. K. Govindaraju, P. Sloan, C. Boyd, and J. Manferdelli. Fast scan algorithms on graphics processors. In ICS'08: Proceedings of the 22nd Annual International Conference on Supercomputing, pages 205-213, 2008.
8. M. Frigo and S. G. Johnson. The design and implementation of FFTW3. Proceedings of the IEEE, 93(2):216-231, 2005.
9. N. Fujimoto. Faster matrix-vector multiplication on GeForce 8800GTX. In Proceedings of the Workshop on Large-Scale Parallel Processing (colocated with IPDPS), pages 1-8, 2008.
10. M. Harris. High performance computing with CUDA. In Tutorial in IEEE SuperComputing, 2007.
11. T. Hastie, R. Tibshirani, and J. Friedman. The elements of statistical learning. Springer, 2001.
12. Eun-Jin Im, Katherine Yelick, and Richard Vuduc. Sparsity: Optimizationframework for sparse matrix kernels. Int. J. High Perform. Comput. Appl., 18(1):135-158, 2004.
13. Ken Kennedy, Bradley Broom, Arun Chauhan, Rob Fowler, John Garvin, Charles Koelbel, Cheryl McCosh, and John Mellor-Crummey. Telescoping languages: A system for automatic generation of domain languages. Proceedings of the IEEE, 93(2):387-408, 2005.
14. S. Lee, T. Johnson, and R. Eigenmann. Cetus - an extensible compiler infrastructure for source-to-source transformation. In In Proceedings of the 16th Annual Workshop on Languages and Compilers for Parallel Computing (LCPC), pages 539-553, 2003.
15. F. Mao and X. Shen. Cross-input learning and discriminative prediction in evolvable virtual machine. In Proceedings of the International Symposium on Code Generation and Optimization (CGO), 2009.
16. R. Marlet, C. Consel, and P. Boinot. Efficient incremental run-time specialization for free. In Proceedings of ACM SIGPLAN Conference on Programming Language Design and Implementation, pages 281-292, Atlanta, GA, May 1999.
17. John Nickolls, Ian Buck, Michael Garland, and Kevin Skadron. Scalable parallel programming with CUDA. ACM Queue, pages 40-53, March/ April 2008.
18. A. Nukada, Y. Ogata, T. Endo, and S. Matsuoka. Bandwidth intensive 3-D FFT kernel for GPUs using CUDA. In SC'08: Proceedings of the 2008 ACM/IEEE Conference on Supercomputing, pages 1-11, 2008.
19. M. Puschel, J.M.F. Moura, J.R. Johnson, D. Padua, M.M. Veloso, B.W. Singer, Jianxin Xiong, F. Franchetti, A. Gacic, Y. Voronenko, K. Chen, R.W. Johnson, and N. Rizzolo. SPIRAL: code generation for DSP transforms. Proceedings of the IEEE, 93(2):232-275, 2005.
20. S. Ryoo, C. I. Rodrigues, S. S. Baghsorkhi, S. S. Stone, D. B. Kirk, and W. W. Hwu. Optimization principles and application performance evaluation of a multithreaded GPU using CUDA. In PPoPP '08: Proceedings of the 13th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, pages 73-82, 2008.
21. S. Ryoo, C. I. Rodrigues, S. S. Stone, S. S. Baghsorkhi, S. Ueng, J. A. Stratton, and W. W. Hwu. Program optimization space pruning for a multithreaded GPU. In CGO'08: Proceedings of the Sixth Annual IEEE/ACM International Symposium on Code Generation and Optimization, pages 195-204, 2008.
22. Mark Silberstein, Assaf Schuster, Dan Geiger, Anjul Patney, and John D. Owens. Efficient computation of sum-products on GPUs through software-managed cache. In Proceedings of the 22nd ACM International Conference on Supercomputing, pages 309-318, June 2008.
23. N. Thomas, G. Tanase, O. Tkachyshyn, J. Perdue, N. M. Amato, and L. Rauchwerger. A framework for adaptive algorithm selection in STAPL. In Proceedings of the Tenth ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, pages 277-288, 2005.
24. M. Voss and R. Eigenmann. High-level adaptive program optimization with ADAPT. In Proceedings of ACM Symposium on Principles and Practice of Parallel Programming, pages 93-102, Snowbird, Utah, June 2001.
25. R. C. Whaley, A. Petitet, and J. Dongarra. Automated empirical optimizations of software and the ATLAS project. Parallel Computing, 27(1-2):3-35, 2001.