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Phase Guided Profiling for Fast Cache Modeling
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)
2012 (English)In: International Symposium on Code Generation and Optimization (CGO'12), ACM Press, 2012, 175-185 p.Conference paper, Published paper (Refereed)
Abstract [en]

Statistical cache models are powerful tools for understanding application behavior as a function of cache allocation. However, previous techniques have modeled only the average application behavior, which hides the effect of program variations over time. Without detailed time-based information, transient behavior, such as exceeding bandwidth or cache capacity, may be missed. Yet these events, while short, often play a disproportionate role and are critical to understanding program behavior.

In this work we extend earlier techniques to incorporate program phase information when collecting runtime profiling data. This allows us to model an application's cache miss ratio as a function of its cache allocation over time. To reduce overhead and improve accuracy we use online phase detection and phase-guided profiling. The phase-guided profiling reduces overhead by more intelligently selecting portions of the application to sample, while accuracy is improved by combining samples from different instances of the same phase.

The result is a new technique that accurately models the time-varying behavior of an application's miss ratio as a function of its cache allocation on modern hardware. By leveraging phase-guided profiling, this work both improves on the accuracy of previous techniques and reduces the overhead.

Place, publisher, year, edition, pages
ACM Press, 2012. 175-185 p.
National Category
Computer Systems
Research subject
Computer Science; Computer Systems
Identifiers
URN: urn:nbn:se:uu:diva-180134DOI: 10.1145/2259016.2259040ISBN: 978-1-4503-1206-6 (print)OAI: oai:DiVA.org:uu-180134DiVA: diva2:548320
Conference
International Symposium on Code Generation and Optimization (CGO), April 2012, San Jose, CA, USA
Projects
CoDeR-MPUPMARC
Available from: 2012-08-30 Created: 2012-08-30 Last updated: 2014-01-09

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Sembrant, AndreasBlack-Schaffer, DavidHagersten, Erik

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