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Learning Cost-Effective Sampling Strategies for Empirical Performance Modeling

Ritter, Marcus ; Calotoiu, Alexandru ; Rinke, Sebastian ; Reimann, Thorsten ; Hoefler, Torsten ; Wolf, Felix (2020)
Learning Cost-Effective Sampling Strategies for Empirical Performance Modeling.
34th IEEE International Parallel and Distributed Processing Symposium (IPDPS'20). New Orleans, USA (18.-22.05.2020)
doi: 10.1109/IPDPS47924.2020.00095
Konferenzveröffentlichung, Bibliographie

Kurzbeschreibung (Abstract)

Identifying scalability bottlenecks in parallel applications is a vital but also laborious and expensive task. Empirical performance models have proven to be helpful to find such limitations, though they require a set of experiments in order to gain valuable insights. Therefore, the experiment design determines the quality and cost of the models. Extra-P is an empirical modeling tool that uses small-scale experiments to assess the scalability of applications. Its current version requires an exponential number of experiments per model parameter. This makes the creation of empirical performance models very expensive, and in some situations even impractical. In this paper, we propose a novel parameter-value selection heuristic, which functions as a guideline for the experiment design, leveraging sparse performance-modeling, a technique that only needs a polynomial number of experiments per model parameter. Using synthetic analysis and data from three different case studies, we show that our solution reduces the average modeling costs by about 85% while retaining 92% of the model accuracy.

Typ des Eintrags: Konferenzveröffentlichung
Erschienen: 2020
Autor(en): Ritter, Marcus ; Calotoiu, Alexandru ; Rinke, Sebastian ; Reimann, Thorsten ; Hoefler, Torsten ; Wolf, Felix
Art des Eintrags: Bibliographie
Titel: Learning Cost-Effective Sampling Strategies for Empirical Performance Modeling
Sprache: Englisch
Publikationsjahr: 14 Juli 2020
Verlag: IEEE
Buchtitel: Proceedings: 2020 IEEE 34th International Parallel and Distributed Processing Symposium
Veranstaltungstitel: 34th IEEE International Parallel and Distributed Processing Symposium (IPDPS'20)
Veranstaltungsort: New Orleans, USA
Veranstaltungsdatum: 18.-22.05.2020
DOI: 10.1109/IPDPS47924.2020.00095
Kurzbeschreibung (Abstract):

Identifying scalability bottlenecks in parallel applications is a vital but also laborious and expensive task. Empirical performance models have proven to be helpful to find such limitations, though they require a set of experiments in order to gain valuable insights. Therefore, the experiment design determines the quality and cost of the models. Extra-P is an empirical modeling tool that uses small-scale experiments to assess the scalability of applications. Its current version requires an exponential number of experiments per model parameter. This makes the creation of empirical performance models very expensive, and in some situations even impractical. In this paper, we propose a novel parameter-value selection heuristic, which functions as a guideline for the experiment design, leveraging sparse performance-modeling, a technique that only needs a polynomial number of experiments per model parameter. Using synthetic analysis and data from three different case studies, we show that our solution reduces the average modeling costs by about 85% while retaining 92% of the model accuracy.

Freie Schlagworte: LOEWE|SF4.0, DFG|323299120, DFG|320898076, BMBF|01IH16008D, DoE|DE-SC0015524, DFG, BMBF, LOEWE
Fachbereich(e)/-gebiet(e): 20 Fachbereich Informatik
20 Fachbereich Informatik > Parallele Programmierung
Zentrale Einrichtungen
Zentrale Einrichtungen > Hochschulrechenzentrum (HRZ)
Zentrale Einrichtungen > Hochschulrechenzentrum (HRZ) > Hochleistungsrechner
Hinterlegungsdatum: 04 Apr 2024 09:04
Letzte Änderung: 27 Jun 2024 12:56
PPN: 519418484
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