Abstract
Modern computer components, both hardware and software, come with many tunable parameters and their parametrization can have a strong impact on their performance. Auto-tuning methods relying on black-box optimization have delivered good results for finding the optimal parametrization of complex computer systems. In this paper, we present a new optimization framework, called the Smart HPC MANager. It provides an out-of-the-box Web application to perform black-box auto-tuning of computer components running on a distributed system for an application submitted by the user. This framework integrates three state-of-the-art heuristics, as well as resampling strategies to deal with the noise due to resource sharing, and pruning strategies to speed-up the convergence process. We demonstrate a possible use-case of this framework by tuning a software I/O accelerator.
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Robert, S., Zertal, S., Couvee, P. (2021). SHAMan: A Flexible Framework for Auto-tuning HPC Systems. In: Calzarossa, M.C., Gelenbe, E., Grochla, K., Lent, R., Czachórski, T. (eds) Modelling, Analysis, and Simulation of Computer and Telecommunication Systems. MASCOTS 2020. Lecture Notes in Computer Science(), vol 12527. Springer, Cham. https://doi.org/10.1007/978-3-030-68110-4_10
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DOI: https://doi.org/10.1007/978-3-030-68110-4_10
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