Effect of auto-tuning with user's knowledge for numerical software

2004 Computing Frontiers Conference

Volumn , Issue , 2004, Pages 12-25

  Katagiri, Takahiro ^a,b   Kise, Kenji ^a,b   Honda, Hiroki ^a   Yuba, Toshitsugu ^a  

^a UNIVERSITY OF ELECTRO COMMUNICATIONS   (Japan)

^b JAPAN SCIENCE AND TECHNOLOGY AGENCY   (Japan)

Author keywords

Auto Tuning; Eigensolver; Numerical library; Parameter optimization; Performance modeling

Indexed keywords

COMPUTATIONAL METHODS; COMPUTER PROGRAMMING LANGUAGES; EIGENVALUES AND EIGENFUNCTIONS; KNOWLEDGE ACQUISITION; MATHEMATICAL MODELS; OPTIMIZATION; PARAMETER ESTIMATION; SOFTWARE AGENTS; SOFTWARE ENGINEERING;

AUTO-TUNING; EIGENSOLVER; NUMERICAL LIBRARY; PARAMETER OPTIMIZATION; PERFORMANCE MODELING;

COMPUTER SCIENCE;

EID: 4143152968     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/977091.977095     Document Type: Conference Paper

References (13)

1. ATLAS project; http://www.netlib.org/atlas/index.html.
2. J. Bilmes, K. Asanović, C.-W. Chin, and J. Demmel. Optimizing matrix multiply using PHiPAC: a portable, high-performance, ANSI C coding methodology. Proceedings of International Conference on Supercomputing 97, pages 340-347, 1997.
3. J. Dongarra and V. Eijkhout. Self-adapting numerical software for next generation applications. The International Journal of High Perform-ance Computing and Applications, 17(2):125-131, 2003.
4. M. Frigo. A fast Fourier transform compiler. In Proceedings of the 1999 ACM SIGPLAN Conference on Programming Language Design and Implementation, pages 169-180, Atlanta, Georgia, May 1999.
5. T. Imamura and K. Naono. An evaluation towards an automatic tuning eigensolver with performance stability. In Proceedings of Symposium on Advanced Computing Systems and Infrastructures (SACSIS)2003, pages 145-152, 2003.
6. T. Katagiri, H. Kuroda, K. Ohsawa, M. Kudoh, and Y. Kanada. Impact of auto-tuning facilities for parallel numerical library. IPSJ Transaction on High Performance Computing Systems, 42(SIG 12 (HPS 4)):60-76, 2001.
7. H. Kuroda, T. Katagiri, and Y. Kanada. Knowledge discovery in auto-tuning parallel numerical library. Progress in Discovery Science, Final Report of the Japanese Discovery Science Project, Lecture Notes in Computer Science, 2281:628-639, 2002.
8. K. Naono and Y. Yamamoto. A framework for development of the library for massively parallel processors with auto-tuning function and with the single memory interface. IPSJ SIG Notes, (2001-HPC-87):25-30, 2001.
9. R. L. Ribler, H. Simitci, and D. A. Reed. The Autopilot performance-directed adaptive control system. Future Generation Computer Systems, special issue (Performance Data Mining), 18(1):175-187, 2001.
10. K. Takahiro, K. Kise, H. Honda, and T. Yuba. FIBER: A general framework for auto-tuning software. Proceedings of The Fifth International Symposium on High Performance Computing, Springer Lecture Notes in Computer Science(2858):146-159, 2003.
11. K. Takahiro, K. Kise, H. Honda, and T. Yuba. FIBER: A framework of installation, before execution-invocation, and run-time optimization layers for auto-tuning software. IS Technical Report, Graduate School of Information Systems, The University of Electro-Communications, UEC-IS-2003-3, May 2003.
12. C. Tapus, I.-H. Ghung, and J. K. Hollingsworth. Active Harmony: Towards automated performance tuning. In Proceedings of High Performance Networking and Computing (SC2002), Baltimore, USA, November 2003.
13. R. Whaley, A. Petitet, and J. J. Dongarra. Automated empirical optimizations of software and the ATLAS project. Parallel Computing, 27:3-35, 2001.