Abstract
We investigate an automatic tuning method for an eigensolver of a dense symmetric matrix. The aim of this paper is to investigate how to select the unrolling depth. To do this, we evaluate the performance of various unrolled reduction loops of the eigensolver for every matrix size from 3000 to 4000 on the Hitachi SR8000/F1 and on the IBM RS/6000 SP3. We also analyze the trend between Byte/Flop and performance for various patterns of loop unrolling. The result shows that the performance is degraded with higher depth of unrolling in some matrix sizes, where it does not occur with lower depth of unrolling. The result also shows that selection of the unrolling depth should be examined in the case of several matrix sizes.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Blimes, J., Asanovic, K., Chin, C.-W., Demmel, J.: Optimizing matrix multiply using PHiPAC: a portable, high-performance, ANSI C coding methodology. In: Proceedings of International Conference on Supercomputing, vol. 97, pp. 340–347 (1997)
Dongarra, J.J., Eijkhout, V.: Self-adapting numerical software for next generation applications. The International Journal of High Performance Computing Applications 17(2), 125–131 (Summer 2003)
Dongarra, J.J., van de Geijn, R.A.: Reduction to condensed form for the eigenvalue problem on distributed architectures. Parallel Computing 18(9), 973–982 (1992)
Frigo, M.: A fast Fourier transform compiler. In: Proceedings of the 1999 ACM SIGPLAN Conference on Programming Language Design and Implementation, Atlanta, Georgia, May 1999, pp. 169–180 (1999)
Katagiri, T., Kise, K., Honda, H., Yuba, T.: FIBER: A General Framework for Auto-Tuning Software. In: Veidenbaum, A., Joe, K., Amano, H., Aiso, H. (eds.) ISHPC 2003. LNCS, vol. 2858, pp. 146–159. Springer, Heidelberg (2003)
Kudoh, M., Kuroda, H., Kanada, Y.: Parallel Blocked Sparse Matrix-Vector Multiplication with Dynamic Parameter Selection Method. In: Sloot, P.M.A., Abramson, D., Bogdanov, A.V., Gorbachev, Y.E., Dongarra, J., Zomaya, A.Y. (eds.) ICCS 2003. LNCS, vol. 2659, pp. 581–591. Springer, Heidelberg (2003)
Kuroda, H., Katagiri, T., Kanada, Y.: Knowledge Discovery in Auto-tuning Parallel Numerical Library. In: Arikawa, S., Shinohara, A. (eds.) Progress in Discovery Science. LNCS (LNAI), vol. 2281, pp. 628–639. Springer, Heidelberg (2002)
Naono, K.: A framework for development of the library for massively parallel processors with auto-tuning function and with the single memory interface. In: SIAM Conference on Parallel Processing for Scientific Computing, San Francisco (2004)
Naono, K., Imamura, T.: An Evaluation towards an Automatic Tuning Eigensolver. IPSJ SIG Notes (2002-HPC-91), 49–54 (1992) (in Japanese)
NetSolve project, http://www.cs.utk.edu/netsolve/
Ninf Project, http://ninf.apgrid.org/
Whaley, R., Petitet, A., Dongarra, J.J.: Automated empirical optimizations of software and the ATLAS project. Parallel Computing 27, 3–35 (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Naono, K., Imamura, T. (2006). An Evaluation Towards Automatically Tuned Eigensolvers. In: Lirkov, I., Margenov, S., Waśniewski, J. (eds) Large-Scale Scientific Computing. LSSC 2005. Lecture Notes in Computer Science, vol 3743. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11666806_48
Download citation
DOI: https://doi.org/10.1007/11666806_48
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-31994-8
Online ISBN: 978-3-540-31995-5
eBook Packages: Computer ScienceComputer Science (R0)