Abstract
Problems such as fault tolerance and scalable synchronization can be efficiently solved using reversibility of applications. Making applications reversible by relying on computation rather than on memory is ideal for large scale parallel computing, especially for the next generation of supercomputers in which memory is expensive in terms of latency, energy, and price. In this direction, a case study is presented here in reversing a computational core, namely, Basic Linear Algebra Subprograms (BLAS), which is widely used in scientific applications. A new Reversible BLAS (RBLAS) library interface has been designed, and a prototype has been implemented with two modes: (1) a memory-mode in which reversibility is obtained by checkpointing to memory, and (2) a computational-mode in which nothing is saved, and restoration is done entirely via inverse computation. The article is focused on detailed performance benchmarking to evaluate the runtime dynamics and performance effects, comparing reversible computation with checkpointing on both traditional CPU platforms and recent GPU accelerator platforms. For BLAS Level-1 subprograms, data indicates over an order of magnitude speed up of reversible computation compared to checkpointing. For BLAS Level-2 and Level-3, a more complex tradeoff is observed between reversible computation and checkpointing, depending on computational and memory complexities of the subprograms.
This paper has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the U.S. Dept. of Energy. Accordingly, the U.S. Government retains and the publisher, by accepting the article for publication, acknowledges that the U.S. Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for U.S. Government purposes.
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
Similar content being viewed by others
Notes
- 1.
The overlap in conventional terminology of “L1” and “L2” between BLAS levels and cache levels is unfortunately unavoidable.
References
Cublas: Common Unified Data Architecture Basic Linear Algebra Subprograms (2012). http://developer.nvidia.com/cublas
Acml: Advanced micro devices core math library (2013). http://developer.amd.com
Barnes, P., Carothers, C., Jefferson, D., LaPre, J.: Warp speed: executing time warp on 1,966,080 cores. In: Proceedings of the ACM SIGSIM Principles of Advanced Discrete Simulation (2013)
Besseron, X., Gautier, T.: Impact of over-decomposition on coordinated checkpoint/rollback protocol. In: Alexander, M., D’Ambra, P., Belloum, A., Bosilca, G., Cannataro, M., Danelutto, M., Di Martino, B., Gerndt, M., Jeannot, E., Namyst, R., Roman, J., Scott, S.L., Traff, J.L., Vallée, G., Weidendorfer, J. (eds.) Euro-Par 2011, Part II. LNCS, vol. 7156, pp. 322–332. Springer, Heidelberg (2012)
Bessho, N., Dohi, T.: Comparing checkpoint and rollback recovery schemes in a cluster system. In: Xiang, Y., Stojmenovic, I., Apduhan, B.O., Wang, G., Nakano, K., Zomaya, A. (eds.) ICA3PP 2012, Part I. LNCS, vol. 7439, pp. 531–545. Springer, Heidelberg (2012)
Dongarra, J., Duff, I., DuCroz, J., Hammarling, S.: A set of level 3 basic linear algebra subprograms. ACM Trans. Math. Softw. 16, 1–28 (1990)
Frank, M.: Introduction to reversible computing: motivation, progress, and challenges. In: International Workshop on Reversible Computing (Special Session at ACM Computing Frontiers) (2005)
Goto, K., Van de Geijn, R.: High-performance implementation of the level-3 blas. ACM Trans. Math. Softw. 35(1), 4:1–4:14 (2008)
He, Y., Ding, C.: Using accurate arithmetics to improve numerical reproducibility and stability in parallel applications. Springer J. Supercomput. 18, 259–277 (2001)
Lawson, C., Hanson, R., Kincaid, D., Krogh, F.: Basic linear algebra subprograms for fortran usage. ACM Trans. Math. Softw. 5, 308–325 (1979)
Li, X., Demmel, J., Baile, D., Henry, G., Hida, Y., Iskandar, J., Kahan, W., Kapur, A., Martin, M.C., Tung, T., Yoo, D.J.: Design, implementation and testing of extended and mixed precision blas. ACM Trans. Math. Softw. 28(2), 206–238 (2002)
Perumalla, K., Park, A.: Reverse computation for rollback-based fault tolerance in large parallel systems. Cluster Comput. 17(2), 303–313 (2014)
Perumalla, K., Park, A., Tipparaju, V.: Discrete event execution with one-sided and two-sided GVT algorithms on 216,000 processor cores. ACM Trans. Model. Comput. Simul. 24(3), 16:1–16:25 (2014)
Perumalla, K.S.: Introduction to Reversible Computing. Computational Science Series. Chapman Hall/CRC Press, Boca Raton (2013). ISBN: 978-1439873403
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Perumalla, K.S., Yoginath, S.B. (2014). Towards Reversible Basic Linear Algebra Subprograms: A Performance Study. In: Gavrilova, M., Tan, C., Thapliyal, H., Ranganathan, N. (eds) Transactions on Computational Science XXIV. Lecture Notes in Computer Science(), vol 8911. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45711-5_4
Download citation
DOI: https://doi.org/10.1007/978-3-662-45711-5_4
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-45710-8
Online ISBN: 978-3-662-45711-5
eBook Packages: Computer ScienceComputer Science (R0)