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AccaSim: An HPC Simulator for Workload Management

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High Performance Computing (CARLA 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 796))

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Abstract

We present AccaSim, an HPC simulator for workload management. Thanks to the scalability and high customizability features of AccaSim, users can easily represent various real HPC system resources, develop dispatching methods and carry out large experiments across different workload sources. AccaSim is thus an attractive tool for conducting controlled experiments in HPC dispatching research.

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Notes

  1. 1.

    http://www.prace-ri.eu/praceannualreports/.

  2. 2.

    http://www2.itif.org/2016-high-performance-computing.pdf.

  3. 3.

    https://www.python.org/events/python-events/.

  4. 4.

    https://pypi.org.

  5. 5.

    https://www.hpc2n.umu.se/resources/hardware/seth.

  6. 6.

    http://www.cs.huji.ac.il/labs/parallel/workload/l_hpc2n/index.html.

  7. 7.

    http://www.top500.org/.

  8. 8.

    Slurm Workload Manager: https://slurm.schedmd.com/.

  9. 9.

    https://www.spec.org/power_ssj2008/.

  10. 10.

    http://www.omnetpp.org/.

References

  1. Acun, B., Jain, N., Bhatele, A., Mubarak, M., Carothers, C.D., Kalé, L.V.: Preliminary evaluation of a parallel trace replay tool for HPC network simulations. In: Hunold, S., Costan, A., Giménez, D., Iosup, A., Ricci, L., Gómez Requena, M.E., Scarano, V., Varbanescu, A.L., Scott, S.L., Lankes, S., Weidendorfer, J., Alexander, M. (eds.) Euro-Par 2015. LNCS, vol. 9523, pp. 417–429. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-27308-2_34

    Chapter  Google Scholar 

  2. Auweter, A., Bode, A., Brehm, M., Brochard, L., Hammer, N., Huber, H., Panda, R., Thomas, F., Wilde, T.: A case study of energy aware scheduling on SuperMUC. In: Kunkel, J.M., Ludwig, T., Meuer, H.W. (eds.) ISC 2014. LNCS, vol. 8488, pp. 394–409. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-07518-1_25

    Google Scholar 

  3. Banerjee, A., Mukherjee, T., Varsamopoulos, G., Gupta, S.K.: Integrating cooling awareness with thermal aware workload placement for HPC data centers. Sustain. Comput. Inf. Syst. 1(2), 134–150 (2011)

    Google Scholar 

  4. Blazewicz, J., Lenstra, J.K., Kan, A.H.G.R.: Scheduling subject to resource constraints: classification and complexity. Discrete Appl. Math. 5(1), 11–24 (1983)

    Article  MathSciNet  MATH  Google Scholar 

  5. Bodas, D., Song, J., Rajappa, M., Hoffman, A.: Simple power-aware scheduler to limit power consumption by HPC system within a budget. In: Proceedings of E2SC@SC, pp. 21–30. IEEE (2014)

    Google Scholar 

  6. Borghesi, A., Collina, F., Lombardi, M., Milano, M., Benini, L.: Power capping in high performance computing systems. In: Pesant, G. (ed.) CP 2015. LNCS, vol. 9255, pp. 524–540. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-23219-5_37

    Google Scholar 

  7. Brandt, J.M., Debusschere, B.J., Gentile, A.C., Mayo, J., Pébay, P.P., Thompson, D.C., Wong, M.: Using probabilistic characterization to reduce runtime faults in HPC systems. In: Proceedings of CCGRID, pp. 759–764. IEEE CS (2008)

    Google Scholar 

  8. Brennan, J., Kureshi, I., Holmes, V.: CDES: an approach to HPC workload modelling. In: Proceedings of DS-RT, pp. 47–54. IEEE CS (2014)

    Google Scholar 

  9. Bridi, T., Bartolini, A., Lombardi, M., Milano, M., Benini, L.: A constraint programming scheduler for heterogeneous high-performance computing machines. IEEE Trans. Parallel Distrib. Syst. 27(10), 2781–2794 (2016)

    Article  Google Scholar 

  10. Feitelson, D.G.: Metrics for parallel job scheduling and their convergence. In: Feitelson, D.G., Rudolph, L. (eds.) JSSPP 2001. LNCS, vol. 2221, pp. 188–205. Springer, Heidelberg (2001). https://doi.org/10.1007/3-540-45540-X_11

    Chapter  Google Scholar 

  11. Feitelson, D.G., Tsafrir, D., Krakov, D.: Experience with using the parallel workloads archive. J. Parallel Distrib. Comput. 74(10), 2967–2982 (2014)

    Article  Google Scholar 

  12. Gómez-Martín, C., Vega-Rodríguez, M.A., Sánchez, J.L.G.: Performance and energy aware scheduling simulator for HPC: evaluating different resource selection methods. Concurrency Comput. Pract. Exp. 27(17), 5436–5459 (2015)

    Article  Google Scholar 

  13. Hurst, W.B., Ramaswamy, S., Lenin, R.B., Hoffman, D.: Modeling and simulation of HPC systems through job scheduling analysis. In: Conference on Applied Research in Information Technology. Acxiom Laboratory of Applied Research (2010)

    Google Scholar 

  14. Jain, N., Bhatele, A., White, S., Gamblin, T., Kalé, L.V.: Evaluating HPC networks via simulation of parallel workloads. In: Proceedings of SC, pp. 154–165. IEEE CS (2016)

    Google Scholar 

  15. Li, Y., Gujrati, P., Lan, Z., Sun, X.: Fault-driven re-scheduling for improving system-level fault resilience. In: Proceedings of ICPP, pp. 39. IEEE CS (2007)

    Google Scholar 

  16. Lucero, A.: Simulation of batch scheduling using real production-ready software tools. In: Proceedings of IBERGRID, pp. 345–356, Netbiblo (2011)

    Google Scholar 

  17. Mubarak, M., Carothers, C.D., Ross, R.B., Carns, P.H.: Enabling parallel simulation of large-scale HPC network systems. IEEE Trans. Parallel Distrib. Syst. 28(1), 87–100 (2017)

    Article  Google Scholar 

  18. Nuñez, A., Fernández, J., García, J.D., García, F., Carretero, J.: New techniques for simulating high performance MPI applications on large storage networks. J. Supercomput. 51(1), 40–57 (2010)

    Article  Google Scholar 

  19. Rodrigo, G.P., Elmroth, E., Östberg, P.-O., Lavanya, R.: ScSF: a scheduling simulation framework. To appear in the Proceedings of JSSPP. Springer (2017)

    Google Scholar 

  20. Skovira, J., Chan, W., Zhou, H., Lifka, D.: The EASY — LoadLeveler API project. In: Feitelson, D.G., Rudolph, L. (eds.) JSSPP 1996. LNCS, vol. 1162, pp. 41–47. Springer, Heidelberg (1996). https://doi.org/10.1007/BFb0022286

    Chapter  Google Scholar 

  21. Snyder, S., Carns, P.H., Latham, R., Mubarak, M., Ross, R.B., Carothers, C.D., Behzad, B., Luu, H.V.T., Byna, S., Prabhat, S.: Techniques for modeling large-scale HPC I/O workloads. In: Proceedings of PMBS@SC, pp. 5:1–5:11. ACM (2015)

    Google Scholar 

  22. Stephen, T., Benini, M.: Using and modifying the BSC slurm workload simulator, Technical report, Slurm User Group Meeting (2015)

    Google Scholar 

  23. Tang, Q., Gupta, S.K.S., Varsamopoulos, G.: Energy-efficient thermal-aware task scheduling for homogeneous high-performance computing data centers: a cyber-physical approach. IEEE Trans. Parallel Distrib. Syst. 19(11), 1458–1472 (2008)

    Article  Google Scholar 

  24. Zhou, Z., Lan, Z., Tang, W., Desai, N.: Reducing energy costs for IBM blue gene/P via power-aware job scheduling. In: Desai, N., Cirne, W. (eds.) JSSPP 2013. LNCS, vol. 8429, pp. 96–115. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-662-43779-7_6

    Google Scholar 

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Acknowledgments

C. Galleguillos is supported by Postgraduate Grant PUCV 2017. We thank Alina Sîrbu for fruitful discussions on the work presented here.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, University of Bologna, Bologna, Italy

    Cristian Galleguillos, Zeynep Kiziltan & Alessio Netti

  2. Escuela de Ing. Informática, Pontificia Universidad Católica de Valparaíso, Valparaíso, Chile

    Cristian Galleguillos

Authors
  1. Cristian Galleguillos
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  2. Zeynep Kiziltan
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  3. Alessio Netti
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Correspondence to Cristian Galleguillos .

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Editors and Affiliations

  1. CSC-CONICET and Universidad de Buenos Aires, Buenos Aires, Argentina

    Esteban Mocskos

  2. Universidad de la República, Montevideo, Uruguay

    Sergio Nesmachnow

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Galleguillos, C., Kiziltan, Z., Netti, A. (2018). AccaSim: An HPC Simulator for Workload Management. In: Mocskos, E., Nesmachnow, S. (eds) High Performance Computing. CARLA 2017. Communications in Computer and Information Science, vol 796. Springer, Cham. https://doi.org/10.1007/978-3-319-73353-1_12

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  • DOI: https://doi.org/10.1007/978-3-319-73353-1_12

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