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Fast Multi-parameter Performance Modeling

Calotoiu, Alexandru ; Beckingsale, David ; Earl, Christopher W. ; Hoefler, Torsten ; Karlin, Ian ; Schulz, Martin ; Wolf, Felix (2016)
Fast Multi-parameter Performance Modeling.
2016 IEEE International Conference on Cluster Computing (CLUSTER). Taipei, Taiwan (12.-16.09.2016)
doi: 10.1109/CLUSTER.2016.57
Konferenzveröffentlichung, Bibliographie

Kurzbeschreibung (Abstract)

Tuning large applications requires a clever exploration of the design and configuration space. Especially on supercomputers, this space is so large that its exhaustive traversal via performance experiments becomes too expensive, if not impossible. Manually creating analytical performance models provides insights into optimization opportunities but is extremely laborious if done for applications of realistic size. If we must consider multiple performance-relevant parameters and their possible interactions, a common requirement, this task becomes even more complex. We build on previous work on automatic scalability modeling and significantly extend it to allow insightful modeling of any combination of application execution parameters. Multi-parameter modeling has so far been outside the reach of automatic methods due to the exponential growth of the model search space. We develop a new technique to traverse the search space rapidly and generate insightful performance models that enable a wide range of uses from performance predictions for balanced machine design to performance tuning.

Typ des Eintrags: Konferenzveröffentlichung
Erschienen: 2016
Autor(en): Calotoiu, Alexandru ; Beckingsale, David ; Earl, Christopher W. ; Hoefler, Torsten ; Karlin, Ian ; Schulz, Martin ; Wolf, Felix
Art des Eintrags: Bibliographie
Titel: Fast Multi-parameter Performance Modeling
Sprache: Englisch
Publikationsjahr: 8 Dezember 2016
Verlag: IEEE
Buchtitel: Proceedings: 2016 IEEE International Conference on Cluster Computing
Veranstaltungstitel: 2016 IEEE International Conference on Cluster Computing (CLUSTER)
Veranstaltungsort: Taipei, Taiwan
Veranstaltungsdatum: 12.-16.09.2016
DOI: 10.1109/CLUSTER.2016.57
Kurzbeschreibung (Abstract):

Tuning large applications requires a clever exploration of the design and configuration space. Especially on supercomputers, this space is so large that its exhaustive traversal via performance experiments becomes too expensive, if not impossible. Manually creating analytical performance models provides insights into optimization opportunities but is extremely laborious if done for applications of realistic size. If we must consider multiple performance-relevant parameters and their possible interactions, a common requirement, this task becomes even more complex. We build on previous work on automatic scalability modeling and significantly extend it to allow insightful modeling of any combination of application execution parameters. Multi-parameter modeling has so far been outside the reach of automatic methods due to the exponential growth of the model search space. We develop a new technique to traverse the search space rapidly and generate insightful performance models that enable a wide range of uses from performance predictions for balanced machine design to performance tuning.

Freie Schlagworte: BMBF|01IH13001G, DFG|SPPEXA 1648, DoE|DE-SC0015524
Fachbereich(e)/-gebiet(e): 20 Fachbereich Informatik
20 Fachbereich Informatik > Parallele Programmierung
Hinterlegungsdatum: 20 Apr 2018 12:22
Letzte Änderung: 14 Mai 2024 10:02
PPN: 518255123
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