Revisiting I/O bandwidth-sharing strategies for HPC applications - Inria - Institut national de recherche en sciences et technologies du numérique
Article Dans Une Revue Journal of Parallel and Distributed Computing Année : 2024

Revisiting I/O bandwidth-sharing strategies for HPC applications

Résumé

This work revisits I/O bandwidth-sharing strategies for HPC applications. When several applications post concurrent I/O operations, well-known approaches include serializing these operations (FCFS) or fair-sharing the bandwidth across them (FairShare). Another recent approach, I/O-Sets, assigns priorities to the applications, which are classified into different sets based upon the average length of their iterations. We introduce several new bandwidth-sharing strategies, some of them simple greedy algorithms, and some of them more complicated to implement, and we compare them with existing ones. Our new strategies do not rely on any a-priori knowledge of the behavior of the applications, such as the length of work phases, the volume of I/O operations, or some expected periodicity. We introduce a rigorous framework, namely steady-state windows, which enables to derive bounds on the competitive ratio of all bandwidth-sharing strategies for three different objectives: minimum yield, platform utilization, and global efficiency. To the best of our knowledge, this work is the first to provide a quantitative assessment of the online competitiveness of any bandwidth-sharing strategy. This theory-oriented assessment is complemented by a comprehensive set of simulations, based upon both synthetic and realistic traces. The main conclusion is that two of our simple and low-complexity greedy strategies significantly outperform FCFS, FairShare and I/O-Sets, and we recommend that the I/O community would implement them for further assessment.
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hal-04714098 , version 1 (30-09-2024)

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Anne Benoit, Thomas Herault, Lucas Perotin, Yves Robert, Frédéric Vivien. Revisiting I/O bandwidth-sharing strategies for HPC applications. Journal of Parallel and Distributed Computing, 2024, 188, ⟨10.1016/j.jpdc.2024.104863⟩. ⟨hal-04714098⟩
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