Improving Analysis in SPMD Applications for Performance Prediction-Reference-Cited by-同舟云学术

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Improving Analysis in SPMD Applications for Performance Prediction

Published:2021 Issue: Volume: Page:387-404
ISSN:2569-7072
Container-title:Advances in Parallel & Distributed Processing, and Applications
language:
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Author:

Tirado Felipe,Wong Alvaro,Rexachs Dolores,Luque Emilio

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-030-69984-0_29

Reference20 articles.

1. N. Attig, P. Gibbon, T. Lippert, Trends in supercomputing: the European path to exascale. Comput. Phys. Commun. 182(9), 2041–2046 (2011)

2. F. Wolf, B. Mohr, J. Dongarra, S. Moore, Automatic analysis of inefficiency patterns in parallel applications. Concurrency Comput. Pract. Exp. 19(11), 1481–1496 (2007)

3. M. Geimer, P. Saviankou, A. Strube, Z. Szebenyi, F. Wolf, B.J.N. Wylie, Further improving the scalability of the Scalasca toolset, in Proceedings of PARA 2010: State of the Art in Scientific and Parallel Computing, Part II: Minisymposium Scalable Tools for High Performance Computing. Lecture Notes in Computer Science, vol. 7134 (2012), pp. 463–474

4. H. Brunst, H.-C. Hoppe, W.E. Nagel, M. Winkler, Performance optimization for large scale computing: the scalable vampir approach., in ICCS (2001), pp. 751–760

5. M. Gerndt, K. Fürlinger, E. Kereku, Periscope: advanced techniques for performance analysis, in PARCO, vol. 33 (2005), pp. 15–26

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1. Performance model for irregular parallel applications.;2023 42nd IEEE International Conference of the Chilean Computer Science Society (SCCC);2023-10-23

2. Efficiency Analysis for AI Applications in HPC Systems. Case Study: K-Means;Computational Science – ICCS 2023;2023

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