Lehr, Jan-Patrick ; Hück, Alexander ; Bischof, Christian (2018):
PIRA: Performance Instrumentation Refinement Automation.
In: AI-SEPS 2018, pp. 1-10, New York, NY, USA, ACM, Proceedings of the 5th ACM SIGPLAN International Workshop on Artificial Intelligence and Empirical Methods for Software Engineering and Parallel Computing Systems, Boston, MA, USA, ISBN 978-1-4503-6067-8,
DOI: 10.1145/3281070.3281071,
[Conference or Workshop Item]
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.
| Item Type: | Conference or Workshop Item |
|---|---|
| Erschienen: | 2018 |
| Creators: | Lehr, Jan-Patrick ; Hück, Alexander ; Bischof, Christian |
| Title: | PIRA: Performance Instrumentation Refinement Automation |
| Language: | English |
| 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. |
| Series: | AI-SEPS 2018 |
| Place of Publication: | New York, NY, USA |
| Publisher: | ACM |
| ISBN: | 978-1-4503-6067-8 |
| Uncontrolled Keywords: | Score-P, automatic program instrumentation, high-performance computing, performance engineering |
| Divisions: | 20 Department of Computer Science 20 Department of Computer Science > Scientific Computing Exzellenzinitiative Exzellenzinitiative > Graduate Schools Exzellenzinitiative > Graduate Schools > Graduate School of Computational Engineering (CE) Zentrale Einrichtungen Zentrale Einrichtungen > University IT-Service and Computing Centre (HRZ) Zentrale Einrichtungen > University IT-Service and Computing Centre (HRZ) > Hochleistungsrechner |
| Event Title: | Proceedings of the 5th ACM SIGPLAN International Workshop on Artificial Intelligence and Empirical Methods for Software Engineering and Parallel Computing Systems |
| Event Location: | Boston, MA, USA |
| Date Deposited: | 26 Nov 2018 14:29 |
| DOI: | 10.1145/3281070.3281071 |
| URL / URN: | http://doi.acm.org/10.1145/3281070.3281071 |
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