Abstract
High-performance computing (HPC) systems are very big and powerful systems, with the main goal of achieving maximum performance of parallel jobs. Many dynamic factors influence the performance which makes this goal a non-trivial task. According to our knowledge, there is no standard tool to automatize performance evaluation through comparing different configurations and helping system administrators to select the best scheduling policy or the best job scheduler. This paper presents the Dynamic Job Scheduler Benchmark (DJSB). It is a configurable tool that compares performance metrics for different scenarios. DJSB receives a workload description and some general arguments such as job submission commands and generates performance metrics and performance plots. To test and present DJSB, we have compared three different scenarios with dynamic resource management strategies using DJSB experiment-driven tool. Results show that just changing some DJSB arguments we can set up and execute quite different experiments, making easy the comparison. In this particular case, a cooperative-dynamic resource management is evaluated compared with other resource management approaches.
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References
HPC Challenge Benchmark website. http://icl.cs.utk.edu/hpcc/
The Human Brain Project. https://www.humanbrainproject.eu/
MareNostrum Supercomputer. https://www.bsc.es/discover-bsc/the-centre/marenostrum
Message Passing Interface Forum. http://www.mpi-forum.org/
Top500 website. https://www.top500.org/
Barcelona Supercomputing Center: The OmpSs Programming Model. https://pm.bsc.es/ompss
Clauss, C., Moschny, T., Eicker, N.: Dynamic process management with allocation-internal co-scheduling towards interactive supercomputing. In: Proceedings of the 1st Workshop Co-Scheduling of HPC Applications, January 2016
Dagum, L., Enon, R.: OpenMP: an industry standard API for shared-memory programming. IEEE Comput. Sci. Eng. 5(1), 46–55 (1998)
Desai, N.: Cobalt: an open source platform for HPC system software research. In: Edinburgh BG/L System Software Workshop (2005)
Dongarra, J.J., Luszczek, P., Petitet, A.: The LINPACK benchmark: past, present and future. Concurr. Comput. Pract. Exp. 15(9), 803–820 (2003)
Duran, A., Ayguadé, E., Badia, R.M., Labarta, J., Martinell, L., Martorell, X., Planas, J.: OmpSs: a proposal for programming heterogeneous multi-core architectures. Parallel Process. Lett. 21(02), 173–193 (2011)
El Maghraoui, K., Desell, T.J., Szymanski, B.K., Varela, C.A.: Malleable iterative MPI applications. Concurr. Comput. Pract. Exp. 21(3), 393–413 (2009)
Fleming, P.J., Wallace, J.J.: How not to lie with statistics: the correct way to summarize benchmark results. Commun. ACM 29(3), 218–221 (1986)
Henderson, R.L.: Job scheduling under the portable batch system. In: Feitelson, D.G., Rudolph, L. (eds.) JSSPP 1995. LNCS, vol. 949, pp. 279–294. Springer, Heidelberg (1995). https://doi.org/10.1007/3-540-60153-8_34
Hoefler, T., Belli, R.: Scientific benchmarking of parallel computing systems: twelve ways to tell the masses when reporting performance results. In: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (2015)
McCalpin, J.D.: Memory bandwidth and machine balance in current high performance computers. In: IEEE Computer Society Technical Committee on Computer Architecture (TCCA) Newsletter, pp. 19–25, December 1995
Smith, J.E.: Characterizing computer performance with a single number. Commun. ACM 31(3), 1202–1206 (1988)
Utrera, G., Tabik, S., Corbalan, J., Labarta, J.: A job scheduling approach for multi-core clusters based on virtual malleability. In: Kaklamanis, C., Papatheodorou, T., Spirakis, P.G. (eds.) Euro-Par 2012. LNCS, vol. 7484, pp. 191–203. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-32820-6_20
Wong, A.T., Oliker, L., Kramer, W.T., Kaltz, T.L., Bailey, D.H.: ESP: a system utilization benchmark. In: ACM/IEEE 2000 Conference on Supercomputing, p. 15. IEEE (2000)
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
Zhou, S., Zheng, X., Wang, J., Delisle, P.: Utopia: a load sharing facility for large, heterogeneous distributed computer systems. Softw. Pract. Exp. 23(12), 1305–1336 (1993)
Acknowledgments
This work is supported by the Spanish Government through Programa Severo Ochoa (SEV-2015-0493), by the Spanish Ministry of Science and Technology (project TIN2015-65316-P), by the Generalitat de Catalunya (grant 2014-SGR-1051), by the European Union’s Horizon 2020 research and innovation program under grant agreement No. 720270 (HBP SGA1).
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Lopez, V., Jokanovic, A., D’Amico, M., Garcia, M., Sirvent, R., Corbalan, J. (2018). DJSB: Dynamic Job Scheduling Benchmark. In: Klusáček, D., Cirne, W., Desai, N. (eds) Job Scheduling Strategies for Parallel Processing. JSSPP 2017. Lecture Notes in Computer Science(), vol 10773. Springer, Cham. https://doi.org/10.1007/978-3-319-77398-8_10
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