Nothing Special   »   [go: up one dir, main page]
Skip to main content
Springer Nature Link
Log in

PAARes: an efficient process allocation based on the available resources of cluster nodes

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

The process allocation is a problem in high performance computing, especially when using heterogeneous architectures involving diverse performance characteristics such as number of cores and their frequencies, multithreading technologies, cache memory etc. In order to improve the application performance, it is necessary to consider which processing units are the most suitable to execute the application processes. In this paper, PAARes (Process Allocation based on the Available Resources) strategy is implemented that automatically collects the system information for the process distribution by considering the processing capacity of each node in a cluster and their available resources. To demonstrate the efficiency and efficacy of the proposed strategy, the NAS (NASA Advanced Supercomputing) parallel benchmark is run on homogeneous and heterogeneous clusters under both dedicated and non-dedicated environments.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Availability of data and materials

Not applicable.

Notes

  1. In OpenMPI a slot is an allocation unit for a process.

  2. Communications where the sender, receiver and message are well defined

  3. Process that generates load on the CPU performing floating point operations.

References

  1. Acun B, Hardy DJ, Kale LV, Li K, Phillips JC, Stone JE (2018) Scalable molecular dynamics with NAMD on the summit system. IBM J Res Dev 62(6):4–149. https://doi.org/10.1147/JRD.2018.2888986

    Article  Google Scholar 

  2. Guo Z, Lu D, Yan Y, Hu S, Liu R, Tan G, Sun N, Jiang W, Liu L, Chen Y, Zhang L, Chen M, Wang H, Jia W (2022) Extending the limit of molecular dynamics with ab initio accuracy to 10 billion atoms. In: Proceedings of the 27th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming. PPoPP’22. Association for Computing Machinery, New York, pp 205–218. https://doi.org/10.1145/3503221.3508425

  3. Morillo J, Vassaux M, Coveney PV, Garcia-Gasulla M (2022) Hybrid parallelization of molecular dynamics simulations to reduce load imbalance. J Supercomput 78(7):9184–9215. https://doi.org/10.1007/s11227-021-04214-4

    Article  Google Scholar 

  4. Pérez-Espinosa A, Aguilar-Cornejo M, Dagdug L (2020) First-passage, transition path, and looping times in conical varying-width channels: comparison of analytical and numerical results. AIP Adv 10(5):055201. https://doi.org/10.1063/5.0004026

    Article  Google Scholar 

  5. Qiu H, Xu C, Li D, Wang H, Li J, Wang Z (2022) Parallelizing and balancing coupled DSMC/PIC for large-scale particle simulations. In: 2022 IEEE International Parallel and Distributed Processing Symposium (IPDPS), pp 390–401. https://doi.org/10.1109/IPDPS53621.2022.00045

  6. Mata AN, Castellanos Abrego NP, Alonso GR, Castro García MA, Garza GL, God ínez Fernández JR (2018) Parallel simulation of sinoatrial node cells synchronization. In: 2018 26th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP), pp 126–133. https://doi.org/10.1109/PDP2018.2018.00025

  7. Cordero-Sánchez S, Rojas-González F, Román-Alonso G, Castro-García MA, Aguilar-Cornejo M, Matadamas-Hernández J (2016) Pore networks subjected to variable connectivity and geometrical restrictions: a simulation employing a multicore system. J Comput Sci 16:177–189. https://doi.org/10.1016/j.jocs.2016.06.003

    Article  Google Scholar 

  8. Ando S, Kaneda M, Suga K (2022) Permeability prediction of fibrous porous media by the lattice Boltzmann method with a fluid-structure boundary reconstruction scheme. J Ind Text 51(4_suppl):6902–6923

    Article  Google Scholar 

  9. Pearson C, Javeed A, Devine K (2022) Machine learning for CUDA+MPI design rules. In: 2022 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), pp 880–889. https://doi.org/10.1109/IPDPSW55747.2022.00144

  10. Alemany S, Nucciarone J, Pissinou N (2021) Jespipe: a plugin-based, open MPI framework for adversarial machine learning analysis. In: 2021 IEEE International Conference on Big Data (Big Data), pp 3663–3670. https://doi.org/10.1109/BigData52589.2021.9671385

  11. Al-Rahayfeh A, Atiewi S, Abuhussein A, Almiani M (2019) Novel approach to task scheduling and load balancing using the dominant sequence clustering and mean shift clustering algorithms. Future Internet. https://doi.org/10.3390/fi11050109

    Article  Google Scholar 

  12. Tyagi R, Gupta SK (2018) A survey on scheduling algorithms for parallel and distributed systems. In: Mishra A, Basu A, Tyagi V (eds) Silicon Photonics & High Performance Computing. Springer, Singapore, pp 51–64

    Chapter  Google Scholar 

  13. Nasa: NASA Advanced Supercomputing Division. https://www.nas.nasa.gov/publications/npb.html#url. Accessed April 2022

  14. Feng H, Misra V, Rubenstein D (2007) PBS: a unified priority-based scheduler. SIGMETRICS Perform Eval Rev 35(1):203–214. https://doi.org/10.1145/1269899.1254906

    Article  Google Scholar 

  15. Zhao T, Gu J, Zhang X (2021) Two-level scheduling technology for heterogeneous clusters using analytical hierarchy processes. In: 2021 IEEE 6th International Conference on Computer and Communication Systems (ICCCS), pp 121–127. https://doi.org/10.1109/ICCCS52626.2021.9449223

  16. Intel: Running an MPI Program. https://www.intel.com/content/www/us/en/develop/documentation/mpi-developer-guide-linux/top/running-applications/running-an-mpi-program.html. Accessed April 2022

  17. mpich: Using the Hydra Process Manager. https://wiki.mpich.org/mpich/index.php/Using_the_Hydra_Process_Manager. Accessed April 2022

  18. openmpi: Running MPI jobs. https://www.open-mpi.org/faq/?category=running. Accessed April 2022

  19. Li K (2008) Optimal load distribution in nondedicated heterogeneous cluster and grid computing environments. J Syst Architect 54(1):111–123. https://doi.org/10.1016/j.sysarc.2007.04.003

    Article  Google Scholar 

  20. Skenteridou K, Karatza HD (2015) Job scheduling in a grid cluster. In: 2015 International Conference on Computer, Information and Telecommunication Systems (CITS), pp 1–5. https://doi.org/10.1109/CITS.2015.7297738

  21. Ullman J (1975) Np-complete scheduling problems. J Comput Syst Sci 10:384–393

    Article  MATH  MathSciNet  Google Scholar 

  22. Cao H, Jin H, Wu X, Wu S, Shi X (2010) DAGMap: efficient and dependable scheduling of DAG workflow job in grid. J Supercomput 51(2):201–223. https://doi.org/10.1007/s11227-009-0284-7

    Article  Google Scholar 

  23. Ganapathi RB, Gopalakrishnan A, McGuire RW (2017) MPI process and network device affinitization for optimal HPC application performance. In: 2017 IEEE 25th Annual Symposium on High-Performance Interconnects (HOTI), pp 80–86. https://doi.org/10.1109/HOTI.2017.12

  24. Jeannot E, Mercier G (2010) Near-optimal placement of MPI processes on hierarchical NUMA architectures. In: Proceedings of the 16th International Euro-Par Conference on Parallel Processing: Part II. Euro-Par’10. Springer, Berlin, pp 199–210. http://dl.acm.org/citation.cfm?id=1885276.1885299

  25. Jeannot E, Mercier G, Tessier F (2014) Process placement in multicore clusters: algorithmic issues and practical techniques. IEEE Trans Parallel Distrib Syst 25(4):993–1002. https://doi.org/10.1109/TPDS.2013.104

    Article  Google Scholar 

  26. Gabriel E, Fagg GE, Bosilca G, Angskun T, Dongarra JJ, Squyres JM, Sahay V, Kambadur P, Barrett B, Lumsdaine A, Castain RH, Daniel DJ, Graham RL, Woodall TS (2004) Open MPI: Goals, concept, and design of a next generation MPI implementation. In: Proceedings, 11th European PVM/MPI Users’ Group Meeting, Budapest, Hungary, pp 97–104

  27. Goglin B (2014) Managing the topology of heterogeneous cluster nodes with hardware locality (HWLOC). In: 2014 International Conference on High Performance Computing Simulation (HPCS), pp 74–81. https://doi.org/10.1109/HPCSim.2014.6903671

  28. Gropp W (2002) MPICH2: a new start for MPI implementations. In: Kranzlmüller D, Volkert J, Kacsuk P, Dongarra J (eds) Recent Advances in Parallel Virtual Machine and Message Passing Interface. Springer, Berlin, pp 7–7

  29. Hursey J, Squyres JM (2013) Advancing application process affinity experimentation: Open MPI’s lama-based affinity interface. In: Proceedings of the 20th European MPI Users’ Group Meeting. EuroMPI’13. ACM, New York, pp 163–168. https://doi.org/10.1145/2488551.2488603

  30. Goglin B (2017) On the overhead of topology discovery for locality-aware scheduling in HPC. In: PDP2017—25th Euromicro International Conference on Parallel, Distributed and Network-Based Processing. Proceedings of the 25th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP2017). IEEE Computer Society, St Petersburg, p 9. https://doi.org/10.1109/PDP.2017.35

  31. Goglin B (2018) Memory footprint of locality information on many-core platforms. In: IEEE (ed.) 6th Workshop on Runtime and Operating Systems for the Many-core Era (ROME 2018), Held in Conjunction with IPDPS, Vancouver, BC, Canada, p 10. https://hal.inria.fr/hal-01644087

  32. Leng T, Ali R, Hsieh J, Mashayekhi V, Rooholamini R (2002) An empirical study of hyper-threading in high performance computing clusters

  33. Marr DT, Binns F, Hill DL, Hinton G, Koufaty DA, Miller AJ, Upton M (2002) Hyper-threading technology architecture and microarchitecture. Intel Technol J 6(1)

  34. openmpi: mpirun. https://www.open-mpi.org/doc/v4.1/man1/mpirun.1.php. Accessed April 2022

Download references

Funding

Not applicable.

Author information

Authors and Affiliations

  1. Universidad Autónoma Metropolitana, Mexico City, Mexico

    J. L. Quiroz-Fabián, G. Román-Alonso, M. A. Castro-García & M. Aguilar-Cornejo

Authors
  1. J. L. Quiroz-Fabián
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. G. Román-Alonso
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. M. A. Castro-García
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. M. Aguilar-Cornejo
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Dr. José Luis and Dr. Graciela wrote the main manuscript text, Dr. Miguel and Dr. Manuel did the experiments, Dr. José Luis and Dr. Manuel prepared figures, and Dr. Graciela and Dr. Miguel prepared tables. All authors conceived of the presented idea, discussed the results and contributed to the final manuscript.

Corresponding author

Correspondence to J. L. Quiroz-Fabián.

Ethics declarations

Conflict of interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Ethical approval

Not applicable.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Quiroz-Fabián, J.L., Román-Alonso, G., Castro-García, M.A. et al. PAARes: an efficient process allocation based on the available resources of cluster nodes. J Supercomput 79, 10423–10441 (2023). https://doi.org/10.1007/s11227-023-05085-7

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-023-05085-7

Keywords

Search

Navigation