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 |
| Zugehörige Links: | |
| 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: | 04 Apr 2024 10:00 |
| PPN: | |
| Zugehörige Links: | |
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