Automatic Adaption of the Sampling Frequency for Detailed Performance Analysis
Research output: Contribution to book/Conference proceedings/Anthology/Report › Conference contribution › Contributed › peer-review
Contributors
Abstract
One of the most urgent challenges in event based performance analysis is the enormous amount of collected data. Combining event tracing and periodic sampling has been a successful approach to allow a detailed event-based recording of MPI communication and a coarse recording of the remaining application with periodic sampling. In this paper, we present a novel approach to automatically adapt the sampling frequency during runtime to the given amount of buffer space, releasing users to find an appropriate sampling frequency themselves. This way, the entire measurement can be kept within a single memory buffer, which avoids disruptive intermediate memory buffer flushes, excessive data volumes, and measurement delays due to slow file system interaction. We describe our approach to sort and store samples based on their order of occurrence in an hierarchical array based on powers of two. Furthermore, we evaluate the feasibility as well as the overhead of the approach with the prototype implementation OTFX based on the Open Trace Format 2, a state-of-the-art Open Source event trace library used by the performance analysis tools Vampir, Scalasca, and Tau.
Details
Original language | English |
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Title of host publication | 2017 17TH IEEE/ACM INTERNATIONAL SYMPOSIUM ON CLUSTER, CLOUD AND GRID COMPUTING (CCGRID) |
Publisher | Wiley-IEEE Press |
Pages | 973-981 |
Number of pages | 9 |
Publication status | Published - 13 Jul 2017 |
Peer-reviewed | Yes |
Conference
Title | 2017 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing |
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Abbreviated title | CCGRID 2017 |
Conference number | 17 |
Duration | 14 - 17 May 2017 |
Degree of recognition | International event |
City | Madrid |
Country | Spain |
External IDs
Scopus | 85027463575 |
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ORCID | /0000-0003-4689-1227/work/110632168 |
Keywords
Keywords
- Algorithms