Skip to main content

2024 | OriginalPaper | Buchkapitel

Algorithm Selection of MPI Collectives Considering System Utilization

verfasst von : Majid Salimi Beni, Sascha Hunold, Biagio Cosenza

Erschienen in: Euro-Par 2023: Parallel Processing Workshops

Verlag: Springer Nature Switzerland

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

MPI collective communications play an important role in coordinating and exchanging data among parallel processes in high performance computing. Various algorithms exist for implementing MPI collectives, each of which exhibits different characteristics, such as message overhead, latency, and scalability, which can significantly impact overall system performance. Therefore, choosing a suitable algorithm for each collective operation is crucial to achieve optimal performance. In this paper, we present our experience with MPI collectives algorithm selection on a large-scale supercomputer and highlight the impact of network traffic and system workload as well as other previously-investigated parameters such as message size, communicator size, and network topology. Our analysis shows that network traffic and system workload can make the performance of MPI collectives highly variable and, accordingly, impact the algorithm selection strategy.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Literatur
1.
Zurück zum Zitat Beni, M.S., Cosenza, B.: An analysis of performance variability on dragonfly+ topology. In: 2022 IEEE International Conference on Cluster Computing (CLUSTER), pp. 500–501. IEEE (2022) Beni, M.S., Cosenza, B.: An analysis of performance variability on dragonfly+ topology. In: 2022 IEEE International Conference on Cluster Computing (CLUSTER), pp. 500–501. IEEE (2022)
2.
Zurück zum Zitat Chunduri, S., Parker, S., Balaji, P., Harms, K., Kumaran, K.: Characterization of MPI usage on a production supercomputer. In: SC18: International Conference for High Performance Computing, Networking, Storage and Analysis, pp. 386–400. IEEE (2018) Chunduri, S., Parker, S., Balaji, P., Harms, K., Kumaran, K.: Characterization of MPI usage on a production supercomputer. In: SC18: International Conference for High Performance Computing, Networking, Storage and Analysis, pp. 386–400. IEEE (2018)
3.
Zurück zum Zitat Faraj, A., Yuan, X., Lowenthal, D.: STAR-MPI: self tuned adaptive routines for MPI collective operations. In: Proceedings of the 20th Annual International Conference on Supercomputing, pp. 199–208 (2006) Faraj, A., Yuan, X., Lowenthal, D.: STAR-MPI: self tuned adaptive routines for MPI collective operations. In: Proceedings of the 20th Annual International Conference on Supercomputing, pp. 199–208 (2006)
5.
Zurück zum Zitat Hunold, S., Bhatele, A., Bosilca, G., Knees, P.: Predicting MPI collective communication performance using machine learning. In: 2020 IEEE International Conference on Cluster Computing (CLUSTER), pp. 259–269. IEEE (2020) Hunold, S., Bhatele, A., Bosilca, G., Knees, P.: Predicting MPI collective communication performance using machine learning. In: 2020 IEEE International Conference on Cluster Computing (CLUSTER), pp. 259–269. IEEE (2020)
6.
Zurück zum Zitat Hunold, S., Carpen-Amarie, A.: Autotuning MPI collectives using performance guidelines. In: Proceedings of the International Conference on High Performance Computing in Asia-Pacific Region, pp. 64–74 (2018) Hunold, S., Carpen-Amarie, A.: Autotuning MPI collectives using performance guidelines. In: Proceedings of the International Conference on High Performance Computing in Asia-Pacific Region, pp. 64–74 (2018)
7.
Zurück zum Zitat Hunold, S., Carpen-Amarie, A.: Reproducible MPI benchmarking is still not as easy as you think. IEEE Trans. Parallel Distrib. Syst. 27, 3617–3630 (2016)CrossRef Hunold, S., Carpen-Amarie, A.: Reproducible MPI benchmarking is still not as easy as you think. IEEE Trans. Parallel Distrib. Syst. 27, 3617–3630 (2016)CrossRef
8.
Zurück zum Zitat Hunold, S., Steiner, S.: OMPICollTune: autotuning MPI collectives by incremental online learning. In: 2022 IEEE/ACM International Workshop on Performance Modeling, Benchmarking and Simulation of High Performance Computer Systems (PMBS), pp. 123–128. IEEE (2022) Hunold, S., Steiner, S.: OMPICollTune: autotuning MPI collectives by incremental online learning. In: 2022 IEEE/ACM International Workshop on Performance Modeling, Benchmarking and Simulation of High Performance Computer Systems (PMBS), pp. 123–128. IEEE (2022)
9.
Zurück zum Zitat Loch, W.J., Koslovski, G.P.: Sparbit: towards to a logarithmic-cost and data locality-aware MPI allgather algorithm. J. Grid Comput. 21, 18 (2023) Loch, W.J., Koslovski, G.P.: Sparbit: towards to a logarithmic-cost and data locality-aware MPI allgather algorithm. J. Grid Comput. 21, 18 (2023)
11.
Zurück zum Zitat Nuriyev, E., Rico-Gallego, J.-A., Lastovetsky, A.: Model-based selection of optimal MPI broadcast algorithms for multi-core clusters. J. Parallel Distrib. Comput. 165, 1–16 (2022)CrossRef Nuriyev, E., Rico-Gallego, J.-A., Lastovetsky, A.: Model-based selection of optimal MPI broadcast algorithms for multi-core clusters. J. Parallel Distrib. Comput. 165, 1–16 (2022)CrossRef
13.
Zurück zum Zitat Beni, M.S., Crisci, L., Cosenza, B.: EMPI: enhanced message passing interface in modern c++. In: 2023 23rd IEEE International Symposium on Cluster, Cloud and Internet Computing (CCGrid), pp. 141–153. IEEE (2023) Beni, M.S., Crisci, L., Cosenza, B.: EMPI: enhanced message passing interface in modern c++. In: 2023 23rd IEEE International Symposium on Cluster, Cloud and Internet Computing (CCGrid), pp. 141–153. IEEE (2023)
14.
Zurück zum Zitat Wilkins, M., Guo, Y., Thakur, R., Dinda, P., Hardavellas, N.: ACCLAiM: advancing the practicality of MPI collective communication autotuning using machine learning. In: 2022 IEEE International Conference on Cluster Computing (CLUSTER), pp. 161–171. IEEE (2022) Wilkins, M., Guo, Y., Thakur, R., Dinda, P., Hardavellas, N.: ACCLAiM: advancing the practicality of MPI collective communication autotuning using machine learning. In: 2022 IEEE International Conference on Cluster Computing (CLUSTER), pp. 161–171. IEEE (2022)
15.
Zurück zum Zitat Wilkins, M., Guo, Y., Thakur, R., Hardavellas, N., Dinda, P., Si, M.: A fact-based approach: making machine learning collective autotuning feasible on exascale systems. In: 2021 Workshop on Exascale MPI (ExaMPI), pp. 36–45. IEEE (2021) Wilkins, M., Guo, Y., Thakur, R., Hardavellas, N., Dinda, P., Si, M.: A fact-based approach: making machine learning collective autotuning feasible on exascale systems. In: 2021 Workshop on Exascale MPI (ExaMPI), pp. 36–45. IEEE (2021)
Metadaten
Titel
Algorithm Selection of MPI Collectives Considering System Utilization
verfasst von
Majid Salimi Beni
Sascha Hunold
Biagio Cosenza
Copyright-Jahr
2024
DOI
https://doi.org/10.1007/978-3-031-48803-0_37

Premium Partner