Abstract
In any well-structured software project, a necessary step consists in validating results relatively to functional expectations. However, in the high-performance computing (HPC) context, this process can become cumbersome due to specific constraints such as scalability and/or specific job launchers. In this paper we present an original validation front-end taking advantage of HPC resources for HPC workloads. By adding an abstraction level between users and the batch manager, our tool JCHRONOSS, drastically reduces test-suite running time, while taking advantage of distributed resources available to HPC developers. We will first introduce validation work-flow challenges before presenting the architecture of our tool and its contribution to HPC validation suites. Eventually, we present results from real test-cases, demonstrating effective speed-up up to 25x compared to sequential validation time – paving the way to more thorough validation of HPC applications.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Cruisecontrol website. http://cruisecontrol.sourceforge.net/
TravisCI website. https://travis-ci.org/
Berg, A.: Jenkins Continuous Integration Cookbook. Packt Publishing Ltd, Birmingham (2012)
Calcote, J.: Autotools: A Practitioner’s Guide to GNU Autoconf, Automake, and Libtool. No Starch Press, San Francisco (2010)
Dean, J., Ghemawat, S.: Mapreduce: simplified data processing on large clusters. Commun. ACM 51(1), 107–113 (2008)
Hoffman, B., Cole, D., Vines, J.: Software process for rapid development of HPC software using cmake. In: DoD High Performance Computing Modernization Program Users Group Conference (HPCMP-UGC), pp. 378–382. IEEE (2009)
Pérache, M., Jourdren, H., Namyst, R.: MPC: a unified parallel runtime for clusters of NUMA machines. In: Luque, E., Margalef, T., Benítez, D. (eds.) Euro-Par 2008. LNCS, vol. 5168, pp. 78–88. Springer, Heidelberg (2008). doi:10.1007/978-3-540-85451-7_9
Vavilapalli, V.K., Murthy, A.C., Douglas, C., Agarwal, S., Konar, M., Evans, R., Graves, T., Lowe, J., Shah, H., Seth, S. et al.: Apache hadoop yarn: Yet another resource negotiator. In: Proceedings of the 4th annual Symposium on Cloud Computing, p. 5. ACM (2013)
Verma, A., Pedrosa, L., Korupolu, M., Oppenheimer, D., Tune, E., Wilkes, J.: Large-scale cluster management at Google with borg. In: Proceedings of the Tenth European Conference on Computer Systems, p. 18. ACM (2015)
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). doi:10.1007/10968987_3
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Adam, J., Pérache, M. (2017). A Parallel and Resilient Frontend for High Performance Validation Suites. In: Dutra, I., Camacho, R., Barbosa, J., Marques, O. (eds) High Performance Computing for Computational Science – VECPAR 2016. VECPAR 2016. Lecture Notes in Computer Science(), vol 10150. Springer, Cham. https://doi.org/10.1007/978-3-319-61982-8_22
Download citation
DOI: https://doi.org/10.1007/978-3-319-61982-8_22
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-61981-1
Online ISBN: 978-3-319-61982-8
eBook Packages: Computer ScienceComputer Science (R0)