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Extracting Clean Performance Models from Tainted Programs

Copik, Marcin ; Calotoiu, Alexandru ; Grosser, Tobias ; Wicki, Nicolas ; Wolf, Felix ; Hoefler, Torsten (2021)
Extracting Clean Performance Models from Tainted Programs.
26th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming. Seoul, South Korea (27. 02.-03.03.2021)
doi: 10.1145/3437801.3441613
Konferenzveröffentlichung, Bibliographie

Kurzbeschreibung (Abstract)

Performance models are well-known instruments to understand the scaling behavior of parallel applications. They express how performance changes as key execution parameters, such as the number of processes or the size of the input problem, vary. Besides reasoning about program behavior, such models can also be automatically derived from performance data. This is called empirical performance modeling. While this sounds simple at the first glance, this approach faces several serious interrelated challenges, including expensive performance measurements, inaccuracies inflicted by noisy benchmark data, and overall complex experiment design, starting with the selection of the right parameters. The more parameters one considers, the more experiments are needed and the stronger the impact of noise. In this paper, we show how taint analysis, a technique borrowed from the domain of computer security, can substantially improve the modeling process, lowering its cost, improving model quality, and help validate performance models and experimental setups.

Typ des Eintrags: Konferenzveröffentlichung
Erschienen: 2021
Autor(en): Copik, Marcin ; Calotoiu, Alexandru ; Grosser, Tobias ; Wicki, Nicolas ; Wolf, Felix ; Hoefler, Torsten
Art des Eintrags: Bibliographie
Titel: Extracting Clean Performance Models from Tainted Programs
Sprache: Englisch
Publikationsjahr: 17 Februar 2021
Verlag: ACM
Buchtitel: PPoPP '21: Proceedings of the ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming
Veranstaltungstitel: 26th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming
Veranstaltungsort: Seoul, South Korea
Veranstaltungsdatum: 27. 02.-03.03.2021
DOI: 10.1145/3437801.3441613
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Kurzbeschreibung (Abstract):

Performance models are well-known instruments to understand the scaling behavior of parallel applications. They express how performance changes as key execution parameters, such as the number of processes or the size of the input problem, vary. Besides reasoning about program behavior, such models can also be automatically derived from performance data. This is called empirical performance modeling. While this sounds simple at the first glance, this approach faces several serious interrelated challenges, including expensive performance measurements, inaccuracies inflicted by noisy benchmark data, and overall complex experiment design, starting with the selection of the right parameters. The more parameters one considers, the more experiments are needed and the stronger the impact of noise. In this paper, we show how taint analysis, a technique borrowed from the domain of computer security, can substantially improve the modeling process, lowering its cost, improving model quality, and help validate performance models and experimental setups.

Freie Schlagworte: DFG|449683531, DFG|323299120, DFG
Fachbereich(e)/-gebiet(e): 20 Fachbereich Informatik
20 Fachbereich Informatik > Parallele Programmierung
Hinterlegungsdatum: 04 Apr 2024 10:00
Letzte Änderung: 18 Jul 2024 10:39
PPN: 519991230
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