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Applying StarPU runtime system to scientific applications: Experiences and lessons learned

Authors: Tzanos, Georgios; Soni, Vineet; Prouveur, Charles; Haefele, Matthieu; Zouzoula, Stavroula; Papadopoulos, Lazaros; Thibault, Samuel; +3 Authors

Applying StarPU runtime system to scientific applications: Experiences and lessons learned

Abstract

Task-based runtime systems are adopted by application developers for their valuable features including flexibility of execution and optimized resource management. However, the use of such advanced programming models in complex HPC applications often requires significant training time and programming effort. In this work, we share experiences and lessons learned from the use of StarPU in three independent projects of various complexity. We reach conclusions, with respect to training, programming effort, and existing challenges, that are useful to the communities of application developers, as well as to the developers of runtime systems. Finally, we suggest extensions to the runtime systems beneficial to application developers.

International audience

Country
France
Keywords

task-based programming models, HPC, StarPU, [INFO.INFO-DC]Computer Science [cs]/Distributed, Parallel, and Cluster Computing [cs.DC]

19 references, page 1 of 2

[1] G. Bosilca, A. Bouteiller, A. Danalis, M. Faverge, T. He´rault, and J. Dongarra, “PaRSEC: A programming paradigm exploiting heterogeneity for enhancing scalability,” Computing in Science and Engineering, vol. 15, no. 6, pp. 36-45, Nov. 2013.

[2] A. YarKhan, “Dynamic task execution on shared and distributed memory architectures,” Ph.D. dissertation, University of Tenessee, 2012.

[3] C. Augonnet, S. Thibault, R. Namyst, and P.-A. Wacrenier, “StarPU: A Unified Platform for Task Scheduling on Heterogeneous Multicore Architectures,” in Euro-Par, ser. Lecture Notes in Computer Science, vol. 5704. Delft, The Netherlands: Springer, Aug. 2009, pp. 863-874. [OpenAIRE]

[4] J. Planas, R. M. Badia, E. Ayguade´, and J. Labarta, “Hierarchical task-based programming with StarSs,” International Journal of High Performance Computing Applications, vol. 23, no. 3, pp. 284-299, 2009.

[5] E. Agullo, B. Bramas, O. Coulaud, E. Darve, M. Messner, and T. Takahashi, “Task-Based FMM for Multicore Architectures,” SIAM Journal on Scientific Computing, vol. 36, no. 1, pp. 66-93, 2014. [OpenAIRE]

[6] A. Podobas, M. Brorsson, and K.-F. Faxe´n, “A comparison of some recent task-based parallel programming models,” in MULTIPROG, 2010. [OpenAIRE]

[7] D. Chasapis, M. Casas, M. Moret o´, R. Vidal, E. Ayguade´, J. Labarta, and M. Valero, “Parsecss: Evaluating the impact of task parallelism in the parsec benchmark suite,” ACM TACO, vol. 12, no. 4, pp. 1-22, 2015.

[8] A. Leist and A. Gilman, “A comparative analysis of parallel programming models for c++,” in ICCGI, 2014.

[9] H. Kaiser, T. Heller, B. Adelstein-Lelbach, A. Serio, and D. Fey, “Hpx: A task based programming model in a global address space,” in PGAS, 2014, pp. 1-11.

[10] “Starpu main webpage,” http://starpu.gforge.inria.fr, 2020.

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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impulse
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views
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