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Runtime power monitoring in high-end processors: methodology and empirical data

Isci, C., Martonosi, M.·2004·22nd Digital Avionics Systems Conference. Proceedings (Cat. No.03CH37449)

Abstract

With power dissipation becoming an increasingly vexing problem across many classes of computer systems, measuring power dissipation of real, running systems has become crucial for hardware and software system research and design. Live power measurements are imperative for studies requiring execution times too long for simulation, such as thermal analysis. Furthermore, as processors become more complex and include a host of aggressive dynamic power management techniques, per-component estimates of power dissipation have become both more challenging as well as more important. In this paper we describe our technique for a coordinated measurement approach that combines real total power measurement with performance-counter-based, per-unit power estimation. The resulting tool offers live total power measurements for Intel Pentium 4 processors, and also provides power breakdowns for 22 of the major CPU subunits over minutes of SPEC2000 and desktop workload execution. As an example application, we use the generated component power breakdowns to identify program power phase behaviour. Overall, this paper demonstrates a processor power measurement and estimation methodology and also gives experiences and empirical application results that can provide a basis for future power-aware research.

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Metadata

Authors
Isci, C., & Martonosi, M.
Venue
22nd Digital Avionics Systems Conference. Proceedings (Cat. No.03CH37449)
Published
2003-01-01
Pages
93-104
ISSN
1072-4451 (ISSN-L)
OpenAlex
W4244593655
Cited by
297
References
0
FWCI
5.26
Type
conference paper
Language
en
Open access
closed

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CoChat. (2026). Citation check: Runtime power monitoring in high-end processors: methodology and empirical data. https://app.cochat.ai/papers/10.1109/micro.2003.1253186
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Isci, C., & Martonosi, M. (2004). Runtime power monitoring in high-end processors: methodology and empirical data. 22nd Digital Avionics Systems Conference. Proceedings (Cat. No.03CH37449). https://doi.org/10.1109/micro.2003.1253186

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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.