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On the Convergence of Malleability and the HPC PowerStack: Exploiting Dynamism in Over-Provisioned and Power-Constrained HPC Systems

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High Performance Computing. ISC High Performance 2022 International Workshops (ISC High Performance 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13387))

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Abstract

Recent High-Performance Computing (HPC) systems are facing important challenges, such as massive power consumption, while at the same time significantly under-utilized system resources. Given the power consumption trends, future systems will be deployed in an over-provisioned manner where more resources are installed than they can afford to power simultaneously. In such a scenario, maximizing resource utilization and energy efficiency, while keeping a given power constraint, is pivotal. Driven by this observation, in this position paper we first highlight the recent trends of resource management techniques, with a particular focus on malleability support (i.e., dynamically scaling resource allocations/requirements for a job), co-scheduling (i.e., co-locating multiple jobs within a node), and power management. Second, we consider putting them together, assess their relationships/synergies, and discuss the functionality requirements in each software component for future over-provisioned and power-constrained HPC systems. Third, we briefly introduce our ongoing efforts on the integration of software tools, which will ultimately lead to the convergence of malleability and power management, as it is designed in the HPC PowerStack initiative.

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References

  1. Deep-sea: Programming environment for european exascale systems. https://www.deep-projects.eu/, Accessed 25 Apr 2022

  2. The hpc powerstack. https://hpcpowerstack.github.io/index.html, LNCS Accessed 25 Apr 2022

  3. Regale: Open architecture for exascale supercomputers. https://regale-project.eu/, Accessed 25 Apr 2022

  4. Top 500. https://www.top500.org/statistics/list/, Accessed 28 Feb 2022

  5. Ahn, D.H., et al.: Flux: overcoming scheduling challenges for exascale workflows. Future Gener. Comput. Syst. 110, 202–213 (2020)

    Article  Google Scholar 

  6. Aupy, G., et al.: Co-scheduling HPC workloads on cache-partitioned CMP platforms. In: CLUSTER, pp. 348–358 (2018)

    Google Scholar 

  7. Bartolini, A., et al.: A pulp-based parallel power controller for future exascale systems. In: ICECS, pp. 771–774 (2019)

    Google Scholar 

  8. Bhadauria, M., et al.: An approach to resource-aware co-scheduling for CMPs. In: ICS, pp. 189–199 (2010)

    Google Scholar 

  9. Borghesi, A., et al.: Examon-x: a predictive maintenance framework for automatic monitoring in industrial iot systems. IEEE Internet Things J. (2021)

    Google Scholar 

  10. Breitbart, J., et al.: Case study on co-scheduling for HPC applications. In: ICPPW, pp. 277–285 (2015)

    Google Scholar 

  11. Breitbart, J., et al.: Dynamic co-scheduling driven by main memory bandwidth utilization. In: CLUSTER, pp. 400–409 (2017)

    Google Scholar 

  12. Breslow, A.D., et al.: Enabling fair pricing on hpc systems with node sharing. In: SC (2013)

    Google Scholar 

  13. Capit, N., et al.: A batch scheduler with high level components. In: CCGrid, vol. 2, pp. 776–783 (2005)

    Google Scholar 

  14. Castain, R.H., et al.: Pmix: process management for exascale environments. Parallel Comput. 79, 9–29 (2018)

    Article  MathSciNet  Google Scholar 

  15. Cesarini, D., et al.: Countdown slack: a run-time library to reduce energy footprint in large-scale mpi applications. IEEE TPDS 31(11), 2696–2709 (2020)

    Google Scholar 

  16. Cochran, R., et al.: Pack & cap: adaptive dvfs and thread packing under power caps. In: MICRO, pp. 175–185 (2011)

    Google Scholar 

  17. Comprés, I., et al.: Infrastructure and api extensions for elastic execution of mpi applications, pp. 82–97. EuroMPI (2016)

    Google Scholar 

  18. Corbalan, J., et al.: EAR: energy management framework for supercomputers. In: Barcelona Supercomputing Center (BSC) Working paper (2019)

    Google Scholar 

  19. D’Amico, M., et al.: Holistic slowdown driven scheduling and resource management for malleable jobs. In: ICPP (2019)

    Google Scholar 

  20. Esmaeilzadeh, H., et al.: Dark silicon and the end of multicore scaling. In: ISCA, pp. 365–376 (2011)

    Google Scholar 

  21. Feitelson, D.G., et al.: Toward convergence in job schedulers for parallel supercomputers. In: JSSPP, pp. 1–26 (1996)

    Google Scholar 

  22. Hennessy, J., Patterson, D.: A new golden age for computer architecture: domain-specific hardware/software co-design, enhanced. In: ISCA (2018)

    Google Scholar 

  23. Kale, L.V., et al.: A malleable-job system for timeshared parallel machines. In: CCGRID, pp. 230–230 (2002)

    Google Scholar 

  24. Mo-Hellenbrand, A., et al.: A large-scale malleable tsunami simulation realized on an elastic mpi infrastructure. In: CF, pp. 271–274 (2017)

    Google Scholar 

  25. Netti, A., et al.: From facility to application sensor data: modular, continuous and holistic monitoring with dcdb. In: SC, pp. 1–27 (2019)

    Google Scholar 

  26. Patki, T., et al.: Exploring hardware overprovisioning in power-constrained, high performance computing. In: ICS, pp. 173–182 (2013)

    Google Scholar 

  27. Patki, T., et al.: Practical resource management in power-constrained, high performance computing. In: HPDC, pp. 121–132 (2015)

    Google Scholar 

  28. Sakamoto, R., et al.: Analyzing resource trade-offs in hardware overprovisioned supercomputers. In: IPDPS, pp. 526–535 (2018)

    Google Scholar 

  29. Sarood, O., et al.: Maximizing throughput of overprovisioned HPC data centers under a strict power budget. In: SC, pp. 807–818 (2014)

    Google Scholar 

  30. Schreiber, M., et al.: Invasive compute balancing for applications with hybrid parallelization. In: SBAC-PAD, pp. 136–143 (2013)

    Google Scholar 

  31. Scogland, T.R., et al.: A power-measurement methodology for large-scale, high-performance computing. In: ICPE, pp. 149–159 (2014)

    Google Scholar 

  32. Shalf, J.: The future of computing beyond moore’s law. Phil. Trans. Roy. Soc. A 378(2166), 20190061 (2020)

    Article  MathSciNet  MATH  Google Scholar 

  33. Utrera, G., et al.: A job scheduling approach for multi-core clusters based on virtual malleability. In: Euro-Par, pp. 191–203 (2012)

    Google Scholar 

  34. Vigouroux, X., et al.: Towards energy consumption application profiling with bull energy software. https://prace-ri.eu/wp-content/uploads/PRACE-at-SC17-Ludovic-Sauge.pdf, Accessed 14 Mar 2022

  35. Yoo, A.B., et al.: Slurm: simple linux utility for resource management. In: JSSPP, pp. 44–60 (2003)

    Google Scholar 

  36. Zhu, Q., et al.: Co-run scheduling with power cap on integrated CPU-GPU systems. In: IPDPS, pp. 967–977 (2017)

    Google Scholar 

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Acknowledgements

We would like to express our sincere gratitude to the anonymous reviewers for their constructive suggestions. This work has received funding under the European Commission’s EuroHPC and H2020 programmes under grant agreement no. 955606 and no. 956560.

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Authors and Affiliations

  1. Technical University of Munich, Garching, Germany

    Eishi Arima, A. Isaías Comprés & Martin Schulz

  2. Leibniz Supercomputing Centre, Garching, Germany

    Martin Schulz

Authors
  1. Eishi Arima
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  2. A. Isaías Comprés
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  3. Martin Schulz
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Corresponding author

Correspondence to Eishi Arima .

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Editors and Affiliations

  1. University of Tennessee, Knoxville, TN, USA

    Hartwig Anzt

  2. University of New Mexico, Albuquerque, NM, USA

    Amanda Bienz

  3. University of Tennessee, Knoxville, TN, USA

    Piotr Luszczek

  4. Université Paris-Saclay, Orsay, France

    Marc Baboulin

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Arima, E., Comprés, A.I., Schulz, M. (2022). On the Convergence of Malleability and the HPC PowerStack: Exploiting Dynamism in Over-Provisioned and Power-Constrained HPC Systems. In: Anzt, H., Bienz, A., Luszczek, P., Baboulin, M. (eds) High Performance Computing. ISC High Performance 2022 International Workshops. ISC High Performance 2022. Lecture Notes in Computer Science, vol 13387. Springer, Cham. https://doi.org/10.1007/978-3-031-23220-6_14

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  • DOI: https://doi.org/10.1007/978-3-031-23220-6_14

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-031-23220-6

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