Abstract
To become auto-adaptive, computer systems should be able to have some knowledge of incoming applications even before launching the application on the system, so that the runtime environment can be customized to the particular needs of this application. In this paper, we propose the architecture of an auto-tuner which relies on record linkage methods to match an incoming application with a database of already known applications. We then present a concrete implementation of this auto-tuner on High Performance Computing (HPC) systems, to submit unknown incoming applications with the best possible parametrization of a smart prefetch strategy by analyzing their metadata. We test this auto-tuner in conditions close to a production environment, and show an improvement of 28% compared to using the default parametrization. The conducted evaluation reveals a negligible overhead of our auto-tuner when running in production and a significant resilience for parallel use on a high-traffic HPC cluster.
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
References
Atos: Tools to improve your efficiency (2018)
Christen, P.: Data Matching: Concepts and Techniques for Record Linkage, Entity Resolution, and Duplicate Detection. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-31164-2
Cohen, W.W., Ravikumar, P., Fienberg, S.E.: A comparison of string distance metrics for name-matching tasks. In: Proceedings of the 2003 International Conference on Information Integration on the Web, IIWEB 2003, pp. 73–78. AAAI Press (2003)
Dunn, H.: Record linkage. Am. J. Public Health 66, 1412–1416 (1946)
Dutot, P., Georgiou, Y., Glesser, D., Lefevre, L., Poquet, M., Rais, I.: Towards energy budget control in HPC. In: EEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid), pp. 381–390 (2017)
Haukka, J., et al.: Incidence of cancer and statin usage-record linkage study. Int. J. Cancer 126, 279–284 (2010)
Jaro, M.A.: Advances in record-linkage methodology as applied to matching the 1985 census of Tampa, Florida. J. Am. Stat. Assoc. 84(406), 414–420 (1989)
Jaro, M.A.: Probabilistic linkage of large public health data files. Stat. Med. 14(5–7), 491–498 (1995)
Yoo, A.B., Jette, M.A., Grondona, M.: SLURM: simple Linux utility for resource management. In: Feitelson, D., Rudolph, L., Schwiegelshohn, U. (eds.) JSSPP 2003. LNCS, vol. 2862, pp. 44–60. Springer, Heidelberg (2003). https://doi.org/10.1007/10968987_3
Kelman, C.W., Bass, J., Holman, D.: Research use of linked health data - a best practice protocol. Aust. NZ J. Public Health 26, 251–255 (2002)
Miller, F.P., Vandome, A.F., McBrewster, J.: Levenshtein Distance: Information Theory, Computer Science, String (Computer Science), String Metric, Damerau? Levenshtein Distance, Spell Checker, Hamming Distance. Alpha Press (2009)
Mitchell, R., Braithwaite, J.: Evidence-informed health care policy and practice: using record linkage to uncover new knowledge. J. Health Serv. Res. Policy 26, 62–67 (2021)
Ranbaduge, T., Christen, P.: A scalable privacy-preserving framework for temporal record linkage. Knowl. Inf. Syst. 62(1), 45–78 (2019). https://doi.org/10.1007/s10115-019-01370-1
Robert, S., Zertal, S., Goret, G.: SHAMan: an intelligent framework for HPC auto-tuning of I/O accelerators (2020)
Robert, S., Zertal, S., Vaumourin, G., Couvée, P.: A comparative study of black-box optimization heuristics for online tuning of high performance computing I/O accelerators. Concurr. Comput. Pract. Exp. e6274. https://doi.org/10.1002/cpe.6274. https://onlinelibrary.wiley.com/doi/abs/10.1002/cpe.6274
Saillant, T., Weill, J.-C., Mougeot, M.: Predicting job power consumption based on RJMS submission data in HPC systems. In: Sadayappan, P., Chamberlain, B.L., Juckeland, G., Ltaief, H. (eds.) ISC High Performance 2020. LNCS, vol. 12151, pp. 63–82. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-50743-5_4
Tanash, M., Dunn, B., Andresen, D., Hsu, W., Yang, H., Okanlawon, A.: Improving HPC system performance by predicting job resources via supervised machine learning. In: Proceedings of the PEARC, pp. 1–8, July 2019
Tromp, M., Ravelli, A.C., Bonsel, G.J., Hasman, A., Reitsma, J.B.: Results from simulated data sets: probabilistic record linkage outperforms deterministic record linkage. J. Clin. Epidemiol. 64, 565–572 (2021)
Winkler, W.E.: Cleaning and using administrative lists: enhanced practices and computational algorithms for record linkage and modeling/editing/imputation. In: Administrative Records for Survey Methodology. Wiley Online Library (2021)
Zhu, Y., Matsuyama, Y., Ohashi, Y., Setoguchi, S.: When to conduct probabilistic linkage vs. deterministic linkage? A simulation study. J. Biomed. Inform. 56, 80–86 (2015)
Acknowledgments
This work was partially supported by the EU project “ASPIDE: Exascale Programming Models for Extreme Data Processing” under grant 801091. We would also like to acknowledge Thibaut Arnoux for his contributions to this paper.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Robert, S., Vincent, L., Zertal, S., Couvée, P. (2021). Record Linkage for Auto-tuning of High Performance Computing Systems. In: Bellatreche, L., Chernishev, G., Corral, A., Ouchani, S., Vain, J. (eds) Advances in Model and Data Engineering in the Digitalization Era. MEDI 2021. Communications in Computer and Information Science, vol 1481. Springer, Cham. https://doi.org/10.1007/978-3-030-87657-9_11
Download citation
DOI: https://doi.org/10.1007/978-3-030-87657-9_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-87656-2
Online ISBN: 978-3-030-87657-9
eBook Packages: Computer ScienceComputer Science (R0)