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Profiles of upcoming HPC Applications and their Impact on Reservation Strategies

Abstract : With the expected convergence between HPC, BigData and AI, newapplications with different profiles are coming to HPC infrastructures.We aim at better understanding the features and needs of theseapplications in order to be able to run them efficiently on HPC platforms.The approach followed is bottom-up: we study thoroughly an emergingapplication, Spatially Localized Atlas Network (SLANT, originating from the neuroscience community) to understand its behavior.Based on these observations, we derive a generic, yet simple, application model (namely, a linear sequence of stochastic jobs). We expect this model to be representative for a large set of upcoming applicationsthat require the computational power of HPC clusters without fitting the typical behavior oflarge-scale traditional applications.In a second step, we show how one can manipulate this generic model in a scheduling framework. Specifically we consider the problem of making reservations (both time andmemory) for an execution on an HPC platform.We derive solutions using the model of the first step of this work.We experimentally show the robustness of the model, even with very few data or with another application, to generate themodel, and provide performance gains
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https://hal.inria.fr/hal-02921487
Contributor : Valentin Honoré <>
Submitted on : Tuesday, August 25, 2020 - 11:39:56 AM
Last modification on : Wednesday, February 3, 2021 - 5:18:38 PM
Long-term archiving on: : Tuesday, December 1, 2020 - 7:23:23 PM

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Ana Gainaru, Brice Goglin, Valentin Honoré, Guillaume Pallez. Profiles of upcoming HPC Applications and their Impact on Reservation Strategies. [Research Report] RR-9359, Inria & Labri, Université Bordeaux. 2020, pp.30. ⟨hal-02921487⟩

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