Lehr, Jan-Patrick ; Hück, Alexander ; Bischof, Christian (2018)
PIRA: Performance Instrumentation Refinement Automation.
Proceedings of the 5th ACM SIGPLAN International Workshop on Artificial Intelligence and Empirical Methods for Software Engineering and Parallel Computing Systems. Boston, MA, USA
doi: 10.1145/3281070.3281071
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
In this paper we present PIRA – an infrastructure for automatic instrumentation refinement for performance analysis. It automates the generation of an initial performance overview measurement and gradually refines it, based on the recorded runtime information. This can help a performance analyst with the time consuming and largely manual, yet mechanical, task of selecting which functions to capture in subsequent measurements. PIRA implements an existing aggregation strategy that heuristically determines which functions to include for initial overview measurements. Moreover, it implements a newly developed heuristic to incorporate profile information and expand instrumentation in hot-spot regions only. The approach is evaluated on different benchmarks, including the SU 2 multi-physics solver package. PIRA is able to generate instrumentation configurations that contain the application’s hot-spot, but generate significantly less overhead when compared to the Score-P reference measurement.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2018 |
| Autor(en): | Lehr, Jan-Patrick ; Hück, Alexander ; Bischof, Christian |
| Art des Eintrags: | Bibliographie |
| Titel: | PIRA: Performance Instrumentation Refinement Automation |
| Sprache: | Englisch |
| Publikationsjahr: | 2018 |
| Ort: | New York, NY, USA |
| Verlag: | ACM |
| Reihe: | AI-SEPS 2018 |
| Veranstaltungstitel: | Proceedings of the 5th ACM SIGPLAN International Workshop on Artificial Intelligence and Empirical Methods for Software Engineering and Parallel Computing Systems |
| Veranstaltungsort: | Boston, MA, USA |
| DOI: | 10.1145/3281070.3281071 |
| URL / URN: | http://doi.acm.org/10.1145/3281070.3281071 |
| Kurzbeschreibung (Abstract): | In this paper we present PIRA – an infrastructure for automatic instrumentation refinement for performance analysis. It automates the generation of an initial performance overview measurement and gradually refines it, based on the recorded runtime information. This can help a performance analyst with the time consuming and largely manual, yet mechanical, task of selecting which functions to capture in subsequent measurements. PIRA implements an existing aggregation strategy that heuristically determines which functions to include for initial overview measurements. Moreover, it implements a newly developed heuristic to incorporate profile information and expand instrumentation in hot-spot regions only. The approach is evaluated on different benchmarks, including the SU 2 multi-physics solver package. PIRA is able to generate instrumentation configurations that contain the application’s hot-spot, but generate significantly less overhead when compared to the Score-P reference measurement. |
| Freie Schlagworte: | Score-P, automatic program instrumentation, high-performance computing, performance engineering |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Scientific Computing Exzellenzinitiative Exzellenzinitiative > Graduiertenschulen Exzellenzinitiative > Graduiertenschulen > Graduate School of Computational Engineering (CE) Zentrale Einrichtungen Zentrale Einrichtungen > Hochschulrechenzentrum (HRZ) Zentrale Einrichtungen > Hochschulrechenzentrum (HRZ) > Hochleistungsrechner |
| Hinterlegungsdatum: | 26 Nov 2018 14:29 |
| Letzte Änderung: | 07 Jan 2021 10:08 |
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