Ilyas, Kashif ; Calotoiu, Alexandru ; Wolf, Felix (2017)
Off-Road Performance Modeling - How to Deal with Segmented Data.
23rd International European Conference on Parallel and Distributed Computing (Euro-Par 2017). Santiago de Compostela, Spanien (28. 08.-01.09.2017)
doi: 10.1007/978-3-319-64203-1_3
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
Besides correctness, scalability is one of the top priorities of parallel programmers. With manual analytical performance modeling often being too laborious, developers increasingly resort to empirical performance modeling as a viable alternative, which learns performance models from a limited amount of performance measurements. Although powerful automatic techniques exist for this purpose, they usually struggle with the situation where performance data representing two or more different phenomena are conflated into a single performance model. This not only generates an inaccurate model for the given data, but can also either fail to point out existing scalability issues or create the appearance of such issues when none are present. In this paper, we present an algorithm to detect segmentation in a sequence of performance measurements and estimate the point where the behavior changes. Our method correctly identified segmentation in more than 80% of 5.2 million synthetic tests and confirmed expected segmentation in three application case studies.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2017 |
| Autor(en): | Ilyas, Kashif ; Calotoiu, Alexandru ; Wolf, Felix |
| Art des Eintrags: | Bibliographie |
| Titel: | Off-Road Performance Modeling - How to Deal with Segmented Data |
| Sprache: | Englisch |
| Publikationsjahr: | 1 August 2017 |
| Verlag: | Springer |
| Buchtitel: | Euro-Par 2017: Parallel Processing |
| Reihe: | Lecture Notes in Computer Science |
| Band einer Reihe: | 10417 |
| Veranstaltungstitel: | 23rd International European Conference on Parallel and Distributed Computing (Euro-Par 2017) |
| Veranstaltungsort: | Santiago de Compostela, Spanien |
| Veranstaltungsdatum: | 28. 08.-01.09.2017 |
| DOI: | 10.1007/978-3-319-64203-1_3 |
| Kurzbeschreibung (Abstract): | Besides correctness, scalability is one of the top priorities of parallel programmers. With manual analytical performance modeling often being too laborious, developers increasingly resort to empirical performance modeling as a viable alternative, which learns performance models from a limited amount of performance measurements. Although powerful automatic techniques exist for this purpose, they usually struggle with the situation where performance data representing two or more different phenomena are conflated into a single performance model. This not only generates an inaccurate model for the given data, but can also either fail to point out existing scalability issues or create the appearance of such issues when none are present. In this paper, we present an algorithm to detect segmentation in a sequence of performance measurements and estimate the point where the behavior changes. Our method correctly identified segmentation in more than 80% of 5.2 million synthetic tests and confirmed expected segmentation in three application case studies. |
| Freie Schlagworte: | DFG|320898076, DFG|SPPEXA 1648, BMBF|01IH16008D, DoE|DE-SC0015524 |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Parallele Programmierung Zentrale Einrichtungen Zentrale Einrichtungen > Hochschulrechenzentrum (HRZ) Zentrale Einrichtungen > Hochschulrechenzentrum (HRZ) > Hochleistungsrechner |
| Hinterlegungsdatum: | 20 Apr 2018 12:23 |
| Letzte Änderung: | 01 Mär 2024 10:08 |
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