Abstract
Tuning numerical libraries has become more difficult over time, as systems get more sophisticated. In particular, modern multicore machines make the behaviour of algorithms hard to forecast and model. In this paper, we tackle the issue of tuning a dense QR factorization on multicore architectures using a fully empirical approach.We exhibit a few strong empirical properties that enable us to efficiently prune the search space. Our method is automatic, fast and reliable. The tuning process is indeed fully performed at install time in less than one hour and ten minutes on five out of seven platforms. We achieve an average performance varying from 97% to 100% of the optimum performance depending on the platform. This work is a basis for autotuning the PLASMA library and enabling easy performance portability across hardware systems.
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References
Frigo, M., Johnson, S.: FFTW: An adaptive software architecture for the FFT. In: Proc. 1998 IEEE Intl. Conf. Acoustics Speech and Signal Processing, vol. 3, pp. 1381–1384. IEEE, Los Alamitos (1998)
Choi, J.W., Singh, A., Vuduc, R.W.: Model-driven autotuning of sparse matrix-vector multiply on GPUs. In: Proc. ACM SIGPLAN Symp. Principles and Practice of Parallel Programming (PPoPP), Bangalore, India (January 2010)
Ansel, J., Chan, C., Wong, Y.L., Olszewski, M., Zhao, Q., Edelman, A., Amarasinghe, S.: Petabricks: A language and compiler for algorithmic choice. In: ACM SIGPLAN Conference on Programming Language Design and Implementation, Dublin, Ireland (June 2009)
Clint Whaley, R., Petitet, A., Dongarra, J.J.: Automated empirical optimizations of software and the atlas project. Parallel Computing 27(1-2), 3–35 (2001)
Volkov, V., Demmel, J.W.: Benchmarking gpus to tune dense linear algebra. In: SC 2008: Proceedings of the ACM/IEEE Conference on Supercomputing, pp. 1–11. IEEE Press, Piscataway (2008)
Tomov, S., Nath, R., Ltaief, H., Dongarra, J.: Dense linear algebra solvers for multicore with gpu accelerators. Accepted for publication at HIPS 2010 (2010)
Quintana-OrtÃ, G., Quintana-OrtÃ, E., van de Geijn, R., Van Zee, F., Chan, E.: Programming matrix algorithms-by-blocks for thread-level parallelism. ACM Trans. Math. Softw. 36(3) (2009)
Buttari, A., Langou, J., Kurzak, J., Dongarra, J.: A class of parallel tiled linear algebra algorithms for multicore architectures. Parallel Computing 35(1), 38–53 (2009)
Agullo, E., Hadri, B., Ltaief, H., Dongarra, J.: Comparative study of one-sided factorizations with multiple software packages on multi-core hardware. In: 2009 International Conference for High Performance Computing, Networking, Storage, and Analysis (SC 2009) (2009)
Agullo, E., Dongarra, J., Nath, R., Tomov, S.: A Fully Empirical Autotuned Dense QR Factorization For Multicore Architectures. Research Report 7526, INRIA (Febuary 2011)
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Agullo, E., Dongarra, J., Nath, R., Tomov, S. (2011). A Fully Empirical Autotuned Dense QR Factorization for Multicore Architectures. In: Jeannot, E., Namyst, R., Roman, J. (eds) Euro-Par 2011 Parallel Processing. Euro-Par 2011. Lecture Notes in Computer Science, vol 6853. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23397-5_19
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DOI: https://doi.org/10.1007/978-3-642-23397-5_19
Publisher Name: Springer, Berlin, Heidelberg
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