Runtime Power Monitoring in High-End Processors: Methodology and Empirical Data
Proceedings of the 36th annual IEEE/ACM International Symposium on Microarchitecture
Power prediction for intel XScale® processors using performance monitoring unit events
ISLPED '05 Proceedings of the 2005 international symposium on Low power electronics and design
Techniques for Multicore Thermal Management: Classification and New Exploration
Proceedings of the 33rd annual international symposium on Computer Architecture
SPEC CPU2006 benchmark descriptions
ACM SIGARCH Computer Architecture News
A system for online power prediction in virtualized environments using Gaussian mixture models
Proceedings of the 47th Design Automation Conference
Chaotic attractor prediction for server run-time energy consumption
HotPower'10 Proceedings of the 2010 international conference on Power aware computing and systems
ACM Transactions on Architecture and Code Optimization (TACO)
Practical power consumption estimation for real life HPC applications
Future Generation Computer Systems
Exploring power behaviors and trade-offs of in-situ data analytics
SC '13 Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis
A review of energy measurement approaches
ACM SIGOPS Operating Systems Review
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This paper proposes to develop a system-wide energy consumption model for servers by making use of hardware performance counters and experimental measurements. We develop a real-time energy prediction model that relates server energy consumption to its overall thermal envelope. While previous studies have attempted system-wide modeling of server power consumption through subsystem models, our approach is different in that it uses a small set of tightly correlated parameters to create a model relating system energy input to subsystem energy consumption. We develop a linear regression model that relates processor power, bus activity, and system ambient temperatures into real-time predictions of the power consumption of long jobs and as result controlling their thermal impact. Using the HyperTransport bus model as a case study and through electrical measurements on example server subsystems, we develop a statistical model for estimating run-time power consumption. Our model is accurate within an error of four percent(4%) as verified using a set of common processor benchmarks.