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Efficient software-based online phase classification
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Systems. (UART)
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Systems. (UART)
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Systems. (UART)
2011 (English)In: International Symposium on Workload Characterization (IISWC'11), IEEE Computer Society, 2011, 104-115 p.Conference paper, Published paper (Refereed)
Abstract [en]

Many programs exhibit execution phases with time-varying behavior. Phase detection has been used extensively to find short and representative simulation points, used to quickly get representative simulation results for long-running applications. Several proposals for hardware-assisted phase detection have also been proposed to guide various forms of optimizations and hardware configurations. This paper explores the feasibility of low overhead phase detection at runtime based entirely on existing features found in modern processors. If successful, such a technology would be useful for cache management, frequency adjustments, runtime scheduling and profiling techniques. The paper evaluates several existing and new alternatives for efficient runtime data collection and online phase detection. ScarPhase (Sample-based Classification and Analysis for Runtime Phases), a new online phase detection library, is presented. It makes extensive usage of the new hardware counter features, introduces a new phase classification heuristic and suggests a way to dynamically adjust the sample rate. ScarPhase exhibits runtime overhead below 2%.

Place, publisher, year, edition, pages
IEEE Computer Society, 2011. 104-115 p.
National Category
Computer Engineering Computer Systems Computer Science
Research subject
Computer Science; Computer Systems
Identifiers
URN: urn:nbn:se:uu:diva-165288DOI: 10.1109/IISWC.2011.6114207ISI: 000299350700016ISBN: 978-1-4577-2063-5 (print)OAI: oai:DiVA.org:uu-165288DiVA: diva2:472831
Conference
IEEE International Symposium on Workload Characterization (IISWC), 6-8 Nov 2011, Austin, TX, USA
Projects
CoDeR-MPUPMARC
Available from: 2012-01-04 Created: 2012-01-04 Last updated: 2012-09-12Bibliographically approved

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Sembrant, AndreasEklöv, DavidHagersten, Erik

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CiteExportLink to record
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Citation style
  • apa
  • ieee
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  • de-DE
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Output format
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